Image diagnosis supporting device

ABSTRACT

An image diagnosis supporting device operates, through digitizing, to apply predetermined image processing to a medical image and to generate a multi-valued image having discrete multiple values. At least one decision making processing routine is then executed on the multi-valued image and/or the medical image; and, from among shadows in the processed image, a focus candidate shadow which is likely to indicate a diseased site is extracted. The extracted focus candidate shadow is then displayed in the medical image so that it is easily identifiable.

TECHNICAL FIELD

The present invention relates to an image diagnosis supporting device,which extracts a shadow that serves as a focus candidate (a possiblydiseased portion) from a medical image by computerized image processingand displays the extracted shadow as is a focus candidate so that it canbe identified.

BACKGROUND OF THE INVENTION

A computerized image diagnosis supporting device analyzes shadows in amedical image by using a computer, and displays a medical imagecontaining a focus candidate shadow selected from the shadows, therebypresenting such medical image to a doctor who has been requested to makea diagnosis. The term “medical image” used herein covers photographicimages photographed with medical image diagnosis devices, such as CTdevices, MRI devices and ultrasonic diagnosis devices, as well asdifference images between past images and current images and the like.As to the method of selecting a focus candidate, several examplesassociated with medical images of lung areas have been reported inmeetings and the like. Among the reports, there is a method ofdiscriminating between a blood vessel shadow having an elongated shapeand a cancer shadow having a shape close to that of a circle in amedical image of a lung area, for example, a “Quoit Filter” (refer toJournal of Computer Aided Diagnosis of Medical Images, Vol. 9, Page 21,November 1999). During a diagnosis of a medical image of a lung area,since the medical image contains not only a shadow of a cancercandidate, but also shadows such as blood vessels, cross sections ofblood vessels and cross sections of bronchi, and various shadows havevarious sizes and shapes, it is desirable that only the shadow of thecancer candidate be extracted from the other shadows and be presented tothe doctor.

However, the above-described image diagnosis supporting device isdifficult to use, because a lot of time-consuming work is required toadjust the parameters for specifying the sizes and shapes of variousshadows.

An object of this invention is to provide an image diagnosis supportingdevice that is capable of reducing the computing time of a computer byhandling shadows of different sizes and shapes in an integrated mannerwhen a decision as to whether a shadow is a focus candidate of a medicalimage is to be automatically made by the use of the computer.

Another object of this invention is to provide an image diagnosissupporting device that is capable of easily and instantaneouslydisplaying a shadow which seems to be an extracted focus candidate.

SUMMARY OF THE INVENTION

To achieve the above object, an image diagnosis supporting deviceaccording to this invention includes digitizing means for applyingpredetermined image processing to a medical image and for generating amulti-valued image, and extracting means for executing at least onedecision process on the multi-valued image and/or the medical imagegenerated by the digitizing means and for extracting a shadow whichseems to be a candidate for a focus, which device identifiably displaysin the medical image the focus candidate shadow extracted by theextracting means.

In addition, since the probability that the focus candidate shadow is afocus (focus certainty) can be determined, when the focus candidateshadow is displayed by being enclosed with a marker or the like, themarker is given a size or a thickness corresponding its focus certainty.

For example, markers are displayed in different colors in the order ofthe highness of the focus certainty, like red, yellow and blue, or areflash-displayed in such a manner as to blink their luminance, or aredisplayed in a combined manner of display in different colors and flashdisplay.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic block diagram of the hardware to which thisinvention is applied;

FIGS. 2 a and 2 b, when combined, comprise a main flowchart of theprocess of focus candidate extraction and display;

FIG. 3 is an image processing flow diagram showing the processing of aCT image according to the flowchart of FIGS. 2 a and 2 b;

FIG. 4 is a diagram showing one example of a display on the CRT of FIG.1;

FIG. 5 is a detailed flowchart showing the first half of Step S81 ofFIG. 2 a;

FIG. 6 is a detailed flowchart showing the second half of Step S81 ofFIG. 2 a;

FIG. 7 is a diagram showing a principle view of the multi-valued imageprocessing of FIGS. 5 and 6;

FIGS. 8 a to 8 c are diagrams showing the concept of shadow extraction;

FIG. 9 is a detailed flowchart showing Step S83 of FIG. 2 a;

FIG. 10 is a flowchart showing details of the shadow extractionprocessing of FIG. 9;

FIG. 11 is a flowchart showing details of the first to third decisionprocessings of Steps S43, S45 and S45 of FIG. 10;

FIG. 12 is a flowchart showing details of decision making subroutines A1to A3 of Steps S72, S74 and S75 of FIG. 11;

FIG. 13 a is a conceptual diagram showing the manner of imaginary loopsset on a CT image, and FIG. 13 b is a graph showing pixel values of eachof the imaginary loops in the processing of FIG. 12;

FIGS. 14 a and 14 b are a conceptual diagram showing a method ofsearching for a pixel whose density is to be found, spirally from thecentral position of a shadow;

FIGS. 15 a and 15 b are conceptual diagrams showing another method ofsearching for a pixel whose density is to be found, spirally from thecentral position of a shadow;

FIGS. 16( a-1), 16(a-2) through 16(c-1), 16(c-2) are conceptual diagramsof sampling points by each of the search methods of FIGS. 15 and 16;

FIGS. 17 a and 17 b are conceptual diagrams of a method of convertingthe shape of a shadow in a multi-valued image into the degree ofdensity;

FIG. 18 is a graph showing a method of determining whether a shadow is afocus candidate shadow or a normal shadow;

FIG. 19 is a diagram showing one example of a CT image in which a focusshadow and a normal shadow exist;

FIGS. 20 a and 20 b, when combined, comprise a detailed flowchart ofeach of the decision making subroutines B1 to B3 of Steps S72, S74 andS75 of FIG. 11;

FIG. 21 a is a conceptual diagram of the case of abnormal shadow, andFIG. 21 b is a graph showing the pixel values in each of the decisionmaking subroutines B1 to B3 of FIGS. 20 a and 20 b;

FIG. 22 a is a conceptual diagram of the case of a blood vesselcross-sectional shadow, and FIG. 22 b is a graph showing the pixelvalues in each of the decision making subroutines B1 to B3 of FIGS. 20 aand 20 b;

FIGS. 23 a and 23 b, when combined, comprise a detailed flowchart ofeach of the decision making subroutines C1 to C3 of Steps S72, S74 andS76 of FIG. 11;

FIG. 24 a is a conceptual diagram and FIG. 24 b is a graph of theprocessing of the decision making subroutines C1 to C3 of FIG. 23;

FIG. 25 is a detailed flowchart of each of decision making subroutinesD1 to D3 of Steps S72, S74 and S76 of FIG. 11;

FIGS. 26 a to 26 d are conceptual diagrams of the processing of thedecision making subroutines D1 to D3 of FIG. 25;

FIG. 27 is a detailed flowchart of a decision making subroutine E1 ofStep S72 of FIG. 11;

FIGS. 28 a and 28 b are conceptual diagrams of the processing of thedecision making subroutine E1 of FIG. 27;

FIG. 29 is a detailed flowchart of a decision making subroutine F1 ofStep S72 of FIG. 11;

FIG. 30 a is a conceptual diagram and FIG. 30 b is a graph of theprocessing of the decision making subroutine F1 of FIG. 29;

FIG. 31 is an image processing flow diagram, representing a modificationof FIG. 3, showing the case where a CT image and images being processedon a bit memory are displayed in a combined form;

FIG. 32 is a diagram showing another example of extracting a normalshadow to be excluded from focus candidate shadows;

FIG. 33 is a diagram showing an example of the display of all images andfocus candidate images in separate windows in a classified manner;

FIG. 34 is a diagram showing one example of a picture for setting theparameters required for the processing of each of the decision makingsubroutines;

FIG. 35 is a diagram showing an example in which a plurality of circleswhich enclose a plurality of focus candidate shadows are displayed in aCT image;

FIGS. 36 a and 36 b are diagrams conceptually showing a method ofprocessing the plurality of circles shown in FIG. 35, and the drawing ofcircular arcs so that the circles do not overlap one another,respectively;

FIG. 37 is a flowchart showing one example of the processing of drawingthe circular arcs shown in FIG. 35;

FIG. 38 is a diagram showing one example of a case where a detectionresult picture, in which a focus candidate shadow is indicated by amarker, and a magnified picture in which the portion of the marker isdisplayed on a magnified scale, are displayed in one picture at the sametime;

FIG. 39 is a diagram showing one example of a display picture in whichimages are displayed in the order of execution of extraction of focuscandidate shadows;

FIGS. 40 a and 40 b are diagrams showing the state in which a CT imageis divided into 16 parts in the horizontal and vertical directions, FIG.40( a) being a view showing the case where the 16 parts are assignednumbers in order from top left to bottom left, and FIG. 40( b) being aview showing the case where the 16 parts are assigned numbers in orderfrom top left to top right;

FIG. 41 is a diagram showing one example of a display picture in which afocus candidate shadow is displayed in order from top left to bottomleft;

FIG. 42 is a diagram showing one example of a display picture in which afocus candidate shadow is displayed in order from top left to top right;

FIG. 43 is a diagram showing the state in which a CT image is dividedinto 40 areas in the horizontal and vertical directions;

FIGS. 44 a to 44 c are diagrams showing a specific example in which, inthe case where it is determined that a shadow is located on a wallportion, a decision is made as to whether the shadow is a focuscandidate shadow, on the basis of the length of contact between theshadow and the wall portion;

FIGS. 45( a) to 45(c) comprise a diagram and graphs showing anotherembodiment of the decision making subroutine;

FIGS. 46 a to 46 d are diagrams showing yet another embodiment of thedecision making subroutine;

FIGS. 47 a and 47 b are conceptual diagrams of the state where, when acomparatively large focus candidate shadow and blood vessel shadowsoverlap one another, the blood vessel shadows are excluded by cutting;

FIG. 48 is a flowchart showing details of the blood vessel cuttingprocessing of FIGS. 47 a and 47 b;

FIG. 49 a is a diagram and FIG. 49 b is a graph showing a specificexample of setting a cutting length in FIG. 48;

FIGS. 50 a and 50 b are diagrams showing a first modification of thedecision making subroutine;

FIG. 51 a is a table and FIG. 51 b is a graph showing the result ofcounting;

FIG. 52 a is a diagram and FIG. 52 b is a graph showing a modificationof the decision making subroutine of FIGS. 50 a and 50 b;

FIGS. 53 a to 53 c are diagrams showing the first half of a secondmodification of the decision making subroutine;

FIGS. 54 a to 54 c are diagrams showing the second half of the secondmodification of the decision making subroutine;

FIG. 55 is a diagram showing a specific example of the case ofidentifying a cancer-accompanying shadow which accompanies a cancer;

FIG. 56 is a flowchart showing details of cancer-accompanying objectdetection processing for detecting a cancer-accompanying shadow;

FIG. 57 is a diagram showing one example of a display picture in whichthe cancer-accompanying shadow detected by the cancer-accompanyingobject detection processing of FIG. 56 is displayed in the state ofoverlapping a marker;

FIG. 58 a is a diagram and FIGS. 58 b and 58 c are graphs showing athird modification of the decision making subroutine;

FIGS. 59 a to 59 c are diagrams showing a modification of the decisionmaking subroutine of FIGS. 58 a to 58 c;

FIGS. 60 a and 60 b are diagrams showing another modification of thedecision making subroutine of FIGS. 58 a to 58 c;

FIGS. 61 a and 61 b are diagrams showing another embodiment of a focuscandidate shadow display in which an arbitrary shadow is displayed witha mouse pointer during the display of a focus candidate shadow;

FIG. 62 is a flowchart showing details of the focus candidate shadowdisplay processing of FIGS. 61 a and 61 b;

FIGS. 63 a and 63 b are diagrams showing a fourth modification of thedecision making subroutine;

FIGS. 64 a and 64 b are diagrams showing a specific manner in whichneedle- or line-shaped shadows called spicules are identified;

FIG. 65 is a conceptual diagram of a processing method, showing amodification of each of the decision making subroutines of FIGS. 27 and29;

FIGS. 66 a to 66 f are diagrams showing a specific example of theprocessing method of FIG. 65;

FIGS. 67 a to 67 d are conceptual diagrams showing a fifth modificationof the decision making subroutine;

FIG. 68 is a table showing the contents of a memory which stores data,such as position information relating to detected focus candidateshadows;

FIGS. 69 a and 69 b are diagrams showing a specific example in whichfocus candidate shadows photographed and extracted in the past aredisplayed together;

FIGS. 70 a and 70 are diagrams showing a modification of the manner ofdisplaying a marker;

FIGS. 71 a and 71 b are diagrams showing a specific example of the casewhere a focus candidate shadow is simply displayed in the state of beingenclosed with a marker, and a specific example of the case where a CTimage in an area enclosed with a marker is displayed in an emphasizedstate, respectively;

FIG. 72 is a flowchart showing a modification of a process where a CTimage having an extracted focus candidate shadow and a CT image havingno extracted focus candidate shadow are displayed in order as akinematic image;

FIG. 73 is a flowchart showing one example of the display processing ofdisplaying the diagnosis result provided by the image diagnosissupporting device according to this invention;

FIGS. 74 a to 74 c are diagrams showing another example of the displayprocessing of displaying the diagnosis result provided by the imagediagnosis supporting device according to this invention;

FIG. 75 is a flowchart showing a modification of a main flowchart of theabnormal shadow detection processing of FIG. 9;

FIGS. 76 a to 76 d are diagrams showing a sixth modification of thedecision making subroutine;

FIG. 77 a is a diagram showing the sixth modification of the decisionmaking subroutine, and FIG. 77 b is a diagram illustrating one exampleof how to extract a shadow which seems not to be a focus;

FIG. 78 is a flowchart showing a modification of the display processingof displaying the diagnosis result provided by the image diagnosissupporting device according to this invention;

FIGS. 79 a to 79 c are diagrams showing one example of a display picturewhich accompanies the display processing of FIG. 78;

FIGS. 80 a to 80 c are diagrams showing a seventh modification of thedecision making subroutine and showing one example of the case where thearea ratio of the total area of the entire focus candidate shadow to thearea of a concave portion formed in an edge portion of the shadow;

FIGS. 81 a and 81 b are diagrams showing a modification of the seventhmodification of the decision making subroutine and showing one exampleof the process of how to extract a bifurcation of a blood vessel shadow;

FIG. 82 is a flowchart showing the seventh modification of the decisionmaking subroutine and a flowchart showing one example of procedures forthe case of finding the area ratio of FIGS. 80 a to 80 c;

FIG. 83 is a diagram showing one example of a display picture whichaccompanies the processing of FIGS. 80 a to 80 c;

FIGS. 84 a and 84 b are diagrams showing a modification of the method offinding the area ratio;

FIGS. 85 a and 85 b are diagrams showing a further modification of themethod of finding the area ratio; and

FIGS. 86 a to 86 c are diagrams showing one example of the case where aspecial shadow including a shadow of a pleural membrane is found.

BEST MODE FOR CARRYING OUT THE INVENTION

Preferred embodiments of an image diagnosis supporting device accordingto this invention will be described with reference to the accompanyingdrawings.

FIG. 1 is a block diagram showing the overall hardware construction ofan image diagnosis supporting device to which this invention is applied.This image diagnosis supporting device displays extracted focuscandidate shadows on the basis of a plurality of tomographic images(such as CT images) that are collected from a target area of a sample bymeans of, for example, an X-ray CT device. The image diagnosissupporting device selectively displays, in addition to focus candidateshadows, shadows of high certainty from among extracted focus candidateshadows or the like, or displays halfway images during this processing.

This image diagnosis supporting device is made up of a centralprocessing unit (CPU) 40 which controls the operation of eachconstituent element, a main memory 42 in which a control program for thedevice is stored, a magnetic disk unit 44 in which a plurality oftomographic image data and a computer program and the like are stored, adisplay memory 46 which temporarily stores image data to be displayed, aCRT display 48 which serves as a display device to display an image onthe basis of image data read out from this display memory 46, a mouse 50for manipulating software switches on a screen, a controller 52 for themouse 50, a keyboard 54 provided with keys and switches for settingvarious parameters, a speaker 58, and a common bus 56 which connects theabove-described constituent elements to one another.

In the illustrated example, only the magnetic disk unit 44 is connectedas a storage device other than the main memory unit 42, but in additionto this magnetic disk 44, a floppy disk drive, a hard disk drive, aCD-ROM drive, a magneto-optical disk (MO) drive, a ZIP drive, a PDdrive, a DVD drive and the like may also be connected. Furthermore, theimage diagnosis supporting device may also be connected to variouscommunication networks, such as a LAN (local area network), the Internetand a telephone line via a communication interface which, is not shown,so that the image diagnosis supporting device can transmit and receiveimage data and program data to and from other computers. In addition,the inputting and outputting of image data may also be implemented insuch a manner that a medical image diagnosis device, such as an X-ray CTdevice and an MRI device, capable of collecting tomographic images ofsamples, is connected to the above-described LAN and the like.

An operational example of the image diagnosis supporting device shown inFIG. 1 will be described below with reference to the accompanyingdrawings. FIG. 2 is a flowchart showing one example of a process to beexecuted by the image diagnosis supporting device. The CPU 40 shown inFIG. 1 operates in accordance with this main flowchart. FIG. 3 is a viewshowing how an CT image is processed according to this main flowchart.FIG. 4 is a view showing one example of a display picture on the CRTdisplay 48. This main flowchart is activated when an operator inputs thename of a patient, who is a target of focus candidate extraction anddisplay processing, in a field “Name of Patient” on the display pictureshown in FIG. 4 and clicks a button “COMPUTE”. Details of the processingindicated by this flowchart will be described below in the order of thesteps thereof.

[Step S80] The CPU 40 reads from the magnetic disk unit 44 a CT image 20(FIG. 3 at (a1)) of the patient corresponding to the patient name shownin FIG. 4 from among CT images of the patient photographed by a CTdevice.

[Step S81] The CPU 40 applies digitization processing to a diagnostictarget organ in the read CT image, and generates a multi-valued image asshown in FIG. 3 at (b1). Details of this digitization processing will bedescribed later.

[Step S82] To execute optimum detection processing corresponding to thearea of the diagnostic target organ or the kind of organ, the CPU 40determines the area or the kind of organ and makes a decision as towhether to proceed to Step S83 or Step S84.

[Step S83] The CPU 40 applies various kinds of image processing to themulti-valued image shown in FIG. 3 at (b1), selects a focus candidateshadow and detects a shadow inferred to be a focus candidate, i.e., anabnormal shadow 22. This abnormal shadow detection processing detectsthe abnormal shadow 22 without using the original CT image, on the basisof only the multi-valued image generated in Step S81. Details of thiswill be described later. By implementing abnormal shadow detectionprocessing on the basis of a multi-valued image as in this embodiment,it is possible to shorten the time required for computer operations andthe like, and it is also possible to ease the burden of computerprocessing.

[Step S84] The CPU 40 applies various kinds of image processing to theCT image of FIG. 3 as seen at (a1) and the multi-valued image of FIG. 3as seen at (b1), and selects a focus candidate shadow and detects ashadow inferred to be a focus candidate, i.e., an abnormal shadow 22.

Incidentally, a decision-in-progress image 24 indicative of the progressof the abnormal shadow detection processing in each of Steps S83 and S84is displayed in parallel form by the side of the CT image 20 of FIG. 3as seen at (a1) on the CRT display 48, as shown in FIG. 4. Incidentally,when the button “COMBINE” shown in FIG. 4 is clicked, thedecision-in-progress image 24 is combined with the CT image 20 inresponse to the click, and the result is displayed. The displayedcontents of the decision-in-progress image 24 are sequentially changedin accordance with the processing of data on the multi-valued image(i.e., according to the stage of extraction of a focus candidateshadow). When the number of extracted abnormal shadows which aredetected through the abnormal shadow detection processing is larger thana predetermined number, the CPU 40 may also provide a display indicativeof decision disabled and bring the processing to an end. The result isconstantly recorded on the magnetic disk. Details of this abnormalshadow detection processing will be described later.

[Step S85] The CPU 40 leaves as a focus portion a focus candidate whichhas been determined to be an abnormal shadow in the above-described StepS83 or S84, or deletes a focus candidate which has not been sodetermined in the above-described Step S83 or S84.

[Step S86] The CPU 40 determines whether the three-dimensional imagestructuring button 3D shown in FIG. 4 has been clicked, i.e., whetherthe three-dimensional image structuring flag is “1” or “0”, and proceedsto Step S87 in the case of “1” (yes). In the case of “0” (no), the CPU40 proceeds to Step S88. Incidentally, the three-dimensional imagestructuring flag can be set to “1” or “0” if the operator arbitrarilyclicks the three-dimensional image structuring button of FIG. 4 as theoccasion demands.

[Step S87] The processing of Step S87 is executed in the case where thedecision made in Step S86 is yes. The CPU 40 starts structuringprocessing for a three-dimensional image from a plurality of CT imagesnear the abnormal shadow. The structuring processing for athree-dimensional image is executed in parallel with the processing ofStep S88, but after the structuring processing for a three-dimensionalimage has been completed, the CPU 40 may also proceed to Step S88 toexecute the processing of Step S88.

[Step S88] In order to enable the abnormal shadow to be easilyidentified, the CPU 40 performs a combining processing to display the CTimage of FIG. 3 as seen at (a1) with color information added thereto, todisplay the abnormal shadow enclosed with a marker M, or to display acolored extracted focus portion or a marker in the original image (CTimage). In FIG. 3 at (a2), there is displayed one example of a combinedimage in which the abnormal shadow is enclosed with the marker M.

[Step S89] The CPU 40 determines whether a multifunction image displaybutton has been turned on, and if the button has been turned on (yes),the CPU 40 proceeds to Step S8A. If the button has not been turned on(no), the CPU 40 proceeds to Step S8B.

[Step S8A] Since the multifunction image display button is in the “on”state, the CPU 40 displays the three-dimensional image structured inStep S87.

[Step S8B] The CPU 40 determines whether an instruction to perform thesame focus candidate extracting and displaying processing on an image ofanother patient has been given by the operator. If the CPU 40 determinesthat an image of another patient is to be displayed (yes), the CPU 40returns to Step S80 and repeatedly executes the same processing. If theCPU 40 determines that an image of another patient is not to bedisplayed (no), the CPU 40 proceeds to Step S8C.

[Step S8C] The CPU 40 determines whether the “END” button shown in FIG.4 has been turned on by the operator. If the CPU 40 determines that thebutton has not been turned on (no), the CPU 40 returns to Step S89 andcontinues normal image display or multifunction image display. If theCPU 40 determines that the button has been turned on (yes), the CPU 40brings the processing to an end.

The multi-valued image processing of Step S81 of FIG. 2 is performed onthe basis of the CT image 20 shown in FIG. 3. This multi-valued imageprocessing is, as shown in FIG. 3, intended to apply predeterminedthresholding processing to a result obtained by calculating a standarddeviation and the like of the original CT image 20 and produce amulti-valued image as shown in FIG. 3 at (a1). FIGS. 5 and 6 areflowcharts showing details of the multi-valued image processing for thediagnostic target organ in Step S81 of FIG. 2. The most basic binaryimage processing in the multi-valued image processing will be describedhereinbelow.

Conventionally, a method of finding a difference between each CT imagehas been used as one of the image processing methods for emphasizing anddisplaying shadows. For example, the difference in CT value betweenpixels at the same address (x, y) is found between two adjacent CTimages each having an image size of 512×512, and this difference in CTvalue is stored at the address (x, y) in the memory, whereby anemphasized image whose shadow is emphasized is obtained. There is also amethod using standard deviation (inclusive of variance). These methodsdo not particularly emphasize the vicinity of the boundary of a shadow,and they extract neither the boundary (edge) of a shadow, nor a shadowonly. On the other hand, this embodiment extracts a shadow in a CT image(particularly, the vicinity of the boundary of the shadow), or adoptsmulti-valued image processing, which enables an extracted shadow to bedisplayed with emphasis. FIG. 7 is a view for theoretically explainingthis multi-valued image processing. Details of the processing indicatedby this main flowchart will be described below in the order of the stepsthereof.

[Step S11] The CPU 40 sets a particular area of predetermined shape tobe the initial position on the CT image. For example, as shown in FIG.7, the CPU 40 sets particular areas (small areas) 12A and 12B eachhaving a square shape of 10×10 pixels within the CT image 20 (atomographic image of the sample), and sets the areas 12A and 12B at aninitial position at the top left corner of the CT image 20. If thecoordinates of the central position of each of these small areas 12A and12B are (X, Y), the coordinates (X, Y) are respectively set to (0, 0).Incidentally, in FIG. 7, the small area 12A is set inside a shadow 15,and the small area 12B is set to overlap the boundary (edge) of a shadow16. Each of these small areas is not limited to a size of 10×10 pixels,and they may also have, for example, a rectangular shape, a diamondshape or a circular shape, other than a square shape. If the centralposition of such a small area differs from the weighted center positionof the same, the weighted center position is given priority, but eitherof the central position or the weighted center position may be selectedfor priority on a case-by-case basis.

[Step S12] The CPU 40 finds an average value AV of the density value (CTvalue) of the small area. The obtained average value AV exhibits a highvalue if the small area exists in the shadow 15 like the small area 12Aof FIG. 7, a low value if the small area does not overlap a shadow, oran approximately middle value if the small area overlaps the shadow 16like the small area 12B.

[Step S13] The CPU 40 finds the average p (xp, yp) of the coordinatevalues of pixels whose density values are not smaller than the averagedensity value AV within the small area, as well as an average m (xm, ym)of the coordinate values of pixels whose density values are smaller thanthe average density value AV. In the case of the small area 12A of FIG.7, average values pA and mA are approximately near the center of thesmall area 12A, and the coordinate positions of both average values pAand mA approximately coincide with each other. On the other hand, in thecase of the small area 12B, an average value pB is approximately nearthe center of the portion of the small area 12B that is superposed onthe shadow 16, and an average value mB is approximately near the centerof the portion of the small area 12B that is not superposed on theshadow 16, and the coordinates of both average values pB and mB areremoved from each other.

[Step S14] The CPU 40 determines the distance D between the coordinates(xp, xy) of the average value p and the coordinates (xm, xm) of theaverage value m. In the case of the small area 12A of FIG. 7, since theaverage values pA and mA are the same, the distance D becomes “0”. Inthe case of the small area 12B, since the average value pB and theaverage value mB are spaced from each other, the distance D becomes acorresponding distance DB. Namely, this distance D tends to be large inthe case where the small area is located near the edge of the shadow, orto be small in the case where the small area does not overlap theshadow.

[Step S15] To make the above-described tendency more remarkable, in thisstep S15, the CPU 40 finds M=g·f(D) as a moment M at the centralcoordinates (X, Y) of the small area on the basis of the distance Dfound in Step S14. This moment M is a value related to (X, Y). Forexample, letting Np be the number of pixels whose density values are notsmaller than the above-described average value AV within the small area,and letting Nm be the number of pixels whose density values are smallerthan the average value AV, each of the moments M1 to M3 found on thebasis of the following equations is defined as the moment M in Step S15:

the moment M1 is M1=Np×Nm×D;

the moment M2 is M2=Nor×D,

(where Nor is the larger of Np and Nm); and

the moment M3 is M3=an existing variance×D,

(where D may be the δ-th power of a value of about 1-3.)

In general, a computation including D is effective. In addition, even inthe decision processing which will be described later, a computationresult including D relative to a focus area can be used for decision.

[Step S16] The CPU 40 adds 1 to the central coordinate X of the smallarea in order to move the small area in the X direction of the image.

[Step S17] The CPU 40 determines whether the value of the coordinate Xof the center of the small area is a maximum (a position where the smallarea is moved beyond the right end of the image), and if the CPU 40determines that the value is a maximum (yes), the CPU 40 proceeds toStep S17. If the CPU 40 determines that the value is not a maximum (no),the CPU 40 returns to Step S12, where the CPU 40 repeats the processingof Step S12 to Step S17 until the value of the central coordinate Xbecomes a maximum.

[Step S18] Since it has been determined in the above-described step S17that the central coordinate X of the small area is a maximum, the CPU 40returns the central coordinate X to an initial value (normally, “0”) inorder to return the small area to the left end of the image.

[Step S19] The CPU 40 adds “1” to the central coordinate Y of the smallarea in order to move the small area in the Y direction of the image.

[Step S20] The CPU 40 determines whether the value of the coordinate Yof the center of the small area is a maximum (a position where the smallarea is moved beyond the right end of the image), and if the CPU 40determines that the value is a maximum (yes), the CPU 40 brings theprocessing to an end, and proceeds to Step S21 of FIG. 6 via a connectorA. If the CPU 40 determines that the value is not a maximum (no), theCPU 40 returns to Step S12, where the CPU 40 repeats the processing ofStep S12 to Step S20 until Y becomes a maximum. In this manner, the CPU40 scans the small area from the top left to the bottom right of the CTimage 20 and sequentially calculates the moment M at the centralcoordinate position of the small area.

A method of extracting pixels located in a shadow or near the boundaryof the shadow from each CT image 20 by using the moment M obtained inthis manner will be described below in accordance with the flowchartshown in FIG. 6.

[Step S21] The CPU 40 reads a constant inputted from the keyboard by theoperator, or a constant stored in advance in the magnetic disk 44 or thelike, as a threshold for determining whether each pixel of the CT image20 is in a shadow or near the boundary of the shadow, and specifies theread constant as a constant.

[Step S22] In order to set a pixel which is a decision target (adecision target pixel) in the initial position which is the top leftcorner of the CT image 20, the CPU 40 set the coordinates (X, Y) of thedecision target pixel) to (0, 0).

[Step S23] The CPU 40 reads the moment M obtained in Step S15 of FIG. 5as to a small area centered about the coordinates (X, Y) of the decisiontarget pixel.

[Step S24] The CPU 40 determines whether the read moment M is largerthan the constant specified in Step S21. If the CPU 40 determines thatthe read moment M is larger (yes), the CPU 40 proceeds to Step S25,whereas if the CPU 40 determines that the read moment M is not larger(no), the CPU 40 jumps to Step S26.

[Step S25] The fact that it has been determined in Step S24 that themoment M is larger than the constant means that the decision targetpixel corresponding to the coordinates (X, Y) corresponds to the shadowor the boundary of the shadow. Accordingly, in this step, the CPU 40extracts the coordinates (X, Y) and stores them in the memory (the mainmemory 42 or the magnetic disk unit 44). Specifically, if the CPU 40 hasdetermined in Step S24 that the moment M is larger than the constant(yes), the CPU 40 sets a binary high level “1” to the coordinates (X,Y). On the other hand, if the CPU 40 determines in Step S24 that themoment M is not larger than the constant (no), the CPU 40 sets a binarylow level “0” to the coordinates (X, Y). In this manner, each set ofcoordinates is set to either of a low level “0” or a high level “1” andis binarized. By binarizing each set of coordinates in this manner, itis possible to express each set of coordinates by one bit, whereby it ispossible to simplify the following processing.

[Step S26] The CPU 40 adds “1” to the coordinate X in order to move thecoordinates of the decision target pixel in the X direction.

[Step S27] The CPU 40 determines whether the value of the coordinate Xof the decision target pixel is a maximum (a position beyond the rightend of the image), and if the CPU 40 determines that the value is amaximum (yes), the CPU 40 proceeds to Step S28. If the CPU 40 determinesthat the value is not a maximum (no), the CPU 40 returns to Step S23,where the CPU 40 repeats the processing of Step S233 to Step S26 until Xbecomes a maximum.

[Step S28] Since the CPU 40 has determined in the above-described stepS27 that the coordinate X of the decision target pixel is a maximum, theCPU 40 resets the coordinate X to “0” in order to return the decisiontarget pixel to the left end, and adds “1” to the coordinate Y of thedecision target pixel in order to move the decision target pixel in theY direction.

[Step S29] The CPU 40 determines whether the coordinate Y of thedecision target pixel is a maximum (a position beyond the bottom end ofthe image), and if the CPU 40 determines that the value is a maximum(yes), the CPU 40 brings the processing to an end. If the CPU 40determines that the value is not a maximum (no), the CPU 40 returns toStep S23, where the CPU 40 repeats the processing of Step S23 to StepS28 until Y becomes a maximum.

In this manner, the CPU 40 scans the decision target pixel from the topleft to the bottom right of the CT image 20 and makes a decision as towhether the decision target pixel corresponds to the shadow or theboundary of the shadow. Through the above-described processing, thecentral point (X, Y) of the small area having the moment M larger thanthe constant, i.e., the coordinate point of the pixel lying in theshadow or the boundary of the shadow, is sequentially stored in thememory (the main memory 42 or the magnetic disk unit 44). Incidentally,in the description of FIGS. 5 and 6, reference has been made tobinarization using a low level “0” and a high level “1”, but the CTimage 20 can be digitized with an arbitrary number of values byspecifying a plurality of constants in Step S21. For example, it ispossible to digitize the CT image with four values by specifying threeconstants C1, C2 and C3 and determining to which of the following casesthe moment M corresponds: the case where the moment M is smaller thanthe constant C1, the case where the moment M is not smaller than theconstant C1 and is smaller than the constant C2, the case where themoment M is not smaller than the constant C2 and is larger than theconstant C3, and the case where the moment M is not smaller than theconstant C3. In the case of four-value digitization, one pixel isexpressed by two bits. Incidentally, if the CT image is to be digitizedwith yet another number of values, a plurality of constants may besimilarly specified so that the CT image can be digitized on the basisof the constants.

FIGS. 8 a to 8 c show the concept of how a shadow is extracted by theabove-described method of extracting a pixel located in a shadow or nearthe boundary of the shadow. When the above-described processing isexecuted on a CT image 21 having a circular shadow which, as shown inFIG. 8 a, exhibits the highest CT value near the center of the shadowand gradually decreases in CT value in the radius direction, a shadow 22of a multi-valued image, the boundary of which is clear, as shown inFIG. 8 b, is stored in the memory and is also displayed on the CRTdisplay 48. In addition, by increasing the constant to be specified inStep S21, a ring-shaped shadow in which a boundary 23 of the shadow isemphasized, as shown in FIG. 8 c is extracted. Accordingly, by variouslychanging the constant to be specified in Step S21, it is possible toextract only the boundary of the shadow or to extract the entire shadow.In addition, the boundary, etc., of the shadow extracted in this mannercan also be displayed with emphasis.

The abnormal shadow detection processing of Step S83 of FIG. 2 a isperformed by using the multi-valued image generated by theabove-described multi-valued image processing. In addition, the abnormalshadow detection processing of Step S84 of FIG. 2 a is performed byusing this multi-valued image and the CT image 20, which is the originalimage. In the case where abnormal shadow detection processing isperformed by using a multi-valued image like that used in Step S83, itis desirable to perform the abnormal shadow detection processing byusing a binary image and a multi-valued image digitized with a largernumber of values (for example, an eight-valued image or a sixteen-valuedimage). In the following description, reference will be made to a casewhere abnormality detection processing is performed by using a binaryimage and the CT image 20. Incidentally, in the case where the abnormalshadow detection processing is to be performed by using only amulti-valued image like in Step S83 of FIG. 2 a, it is similarlypossible to cope with the abnormal shadow detection processing byreading the CT image 20 as the multi-valued image.

FIG. 9 is a view showing the main flowchart of the abnormal shadowdetection processing. The abnormal shadow detection processing of FIG. 9extracts only pixels belonging to the range of predetermined CT values(pixel values) from a medical image and generates a medical image for adecision target, according to a parameter indicative of the kind ofshadow, such as a small shadow, a large shadow, a ground glass opacityor a high-density shadow. Namely, there are kinds of shadows, such as asmall shadow, a large shadow, a ground glass opacity and a high-densityshadow. It is empirically confirmed that each of the shadowsdistinctively appears in a predetermined pixel value range within amedical image. For example, small shadows and large shadows remarkablyappear in the range of pixel values (CT values) from −800 to 0, groundglass opacities in the range of from −800 to −400, and high-densityshadows in the range of from −400 to 0. Accordingly, in this embodiment,only pixels belonging to a predetermined range of pixel values areextracted from a medical image according to the kind of shadow, a newtarget medical image is generated, and extracting processing for a focuscandidate shadow is performed on the basis of the new target medicalimage. In addition, in the case of ground glass opacities, since a focusoccurs in many cases in the periphery of a lung region, it is effectiveto perform separate processing methods on a central portion and on aperipheral portion.

Small shadow detection processing performs abnormal shadow detectionprocessing on a shadow in a decision target medical image made up ofpixels which belong to the range of CT values from −800 to 0 within theCT image 20. Large shadow detection processing performs abnormal shadowdetection processing on a shadow in a decision target medical image madeup of pixels which belong to the range of CT values from −800 to 0.Glass-shaped shadow detection processing performs abnormal shadowdetection processing on a shadow in a decision target medical image madeup of pixels which belong to the range of CT values from −800 to −400.High-density shadow detection processing performs abnormal shadowdetection processing on a shadow in a decision target medical image madeup of pixels which belong to the range of CT values from −400 to 0.Incidentally, the multi-valued image extracting processing of Step S81of FIG. 2 a may be performed on the basis of this target medical image,so that the abnormal shadow detection processing of Step S83 and StepS84 of FIG. 2 a is performed by using the obtained multi-valued image.Accordingly, in the following description, the term “CT image” includesthis target medical image and a multi-valued image, and the terms “pixelvalue” and “density value” include pixel values in a multi-valued image.

FIG. 10 is a view showing details of each of these types of shadowdetection processing. The types of shadow processing executed in theabnormal shadow detection processing shown in FIG. 9 are approximatelycommon to one another except that predetermined values Ra and Rb used inSteps S41, S44 and S46 differ among the types of shadow processing. Thesize of a shadow is expressed by the number of pixels which constitutethe shadow. There exist various focus shadows of different sizes, suchas small shadows, large shadows, ground glass opacitys or high-densityshadows. In the case where decision processing is performed on theseshadows, similar processing is executed on each of the shadows by usinga parameter corresponding to the size of each of the shadows. However,in the case where a shadow itself is small or large, or where shadowsappear at different locations, there occurs the problem that if the samedecision processing is performed with the same parameter, the accuracybecomes lower. Therefore, it is necessary to increase or decrease aparameter according to the size of each shadow. In this embodiment, if ashadow is smaller than the predetermined value Ra, the image of theshadow is enlarged; whereas, if a shadow is larger than thepredetermined value Rb, the image of the shadow is reduced, whereby theimage of the shadow is converted into a shadow having a size whichenables decision processing to be most efficiently performed on theshadow, and the decision processing is performed on the convertedshadow. Details of this shadow detection processing will be describedbelow, step by step.

[Step S41] The CPU 40 determines whether the size (in this case, thenumber of pixels) of a shadow, which constitutes a detection target, issmaller than the predetermined value Ra; and, if the CPU 40 determinesthat the size of the shadow is smaller (yes), the CPU 40 proceeds toStep S42; whereas, if the CPU 40 determines that the size of the shadowis not smaller (yes), the CPU 40 jumps to Step S44.

[Step S42] Since it has been determined in Step S41 that the size of theshadow is smaller, the CPU 40 enlarges the shadow image to a sizecorresponding to a parameter to be used in the first decision processingof Step S43. In this case, the pixel value between each pixel isdetermined by interpolation processing.

[Step S43] The CPU 40 executes the first decision processing on theshadow that was enlarged in Step S42.

[Step S44] The CPU 40 determines whether the size of the shadow, whichconstitutes a decision target, is not smaller than the predeterminedvalue Ra and not larger than the predetermined value Rb or more, i.e.,whether the size of the shadow is within a predetermined range. If theCPU 40 determines that the size of the shadow is within thepredetermined range (yes), the CPU 40 proceeds to Step S45; whereas, ifthe CPU 40 determines that the size of the shadow is not within thepredetermined range (no), the CPU 40 jumps to Step S46.

[Step S45] The CPU 40 executes second decision processing on the shadowwhose size is within the predetermined range of Step S44.

[Step S46] The CPU 40 determines whether the size of the shadowextracted according to each CT value is larger than the predeterminedvalue Rb; and, if the CPU 40 determines that the size of the shadow islarger (yes), the CPU 40 proceeds to Step S47; whereas, if the CPU 40determines that the size of the shadow is not larger (no), the CPU 40brings the processing to an end and proceeds to the next shadow decisionprocessing shown in FIG. 9.

[Step S47] Since the CPU 40 determines in Step S46 that the size of theshadow is larger than the predetermined value Rb, the CPU 40 reduces theimage of the shadow to a size corresponding to a parameter to be used inthe third decision processing of Step S48.

[Step S48] The CPU 40 executes the third decision processing on theshadow that has been reduced in Step S47, and then proceeds to theshadow decision processing shown in FIG. 9 (large-shadow detectionprocessing, ground glass opacity detection processing or high-densityshadow detection processing). Incidentally, although the number ofpixels are used as the size of the shadow, the maximum diameter or theminimum diameter of the shadow may also be used. In this case, it ispreferable to set a minimum diameter of about 7 mm to the predeterminedvalue Ra and a maximum diameter of about 21 mm to the predeterminedvalue Rb.

FIG. 11 is a flowchart showing details of the first to third decisionprocessing of Steps S43, S45 and S48 of FIG. 10. The first to thirddecision processing are approximately common to one another except thatvarious different parameters are used in the respective decision makingsubroutines of Steps S72, S74 and S76. The first decision processing isexecuted on an image which has been enlarged by Step S42 of the shadowdecision, processing shown in FIG. 10. The second decision processing isexecuted on an image whose shadow size has been determined asaccommodated within a predetermined range, by Step S44 of the shadowdecision processing shown in FIG. 10. The third decision processing isexecuted on an image which has been reduced by Step S47 of the shadowdecision processing shown in FIG. 10. In each of the first to thirddecision processing, a combination of decision making subroutines ischanged according to the slice thickness of each CT image.

In a medical image, a shadow such as a cancer, a blood vessel, a crosssection of a blood vessel, a cross section of a bronchus and the likeare photographed together. As the slice thickness of a medical image ismade different, a particular shadow of the shadows contained in themedical image becomes clear or obscure. For example, if the slicethickness is small, a shadow corresponding to a blood vessel extremelydiminishes and becomes difficult to recognize. As the slice thicknessincreases, the shadow of the blood vessel clearly appears. Accordingly,in the case where the slice thickness is small, it is necessary to makea decision as to whether the shadow is a blood vessel shadow or a focuscandidate shadow. Contrarily, in the case where the slice thickness islarge, it is possible to clearly identify the shadow of a blood vessel,so that decision processing is not needed. In addition, in the casewhere a shadow is bound to a lung wall, as shown in FIG. 44, it isdesirable to change processing according to the location where theshadow is present.

Thus, processing is performed with a plurality of specialized decisionmaking subroutines, and, finally, a logical OR operation is carried outwith respect to all the subroutine results. This embodiment is providedwith first combined processing including decision processing E1 and E2necessary for shadow decision in the case of a small slice thickness, aswell as second and third combined processing, not including the decisionprocessing E1 and E2. One of these first to third processes areappropriately selected according to slice thicknesses Sa and Sb. Detailsof the first to third decision processing will be described below in theorder of the steps thereof.

[Step S71] The CPU 40 determines whether the slice thickness of the CTimage 20 is larger than the predetermined value Sa (for example, 10 mm).If the CPU 40 determines that the slice thickness is larger (yes), theCPU 40 proceeds to the next step S72, whereas if the CPU 40 determinesthat the slice thickness is not larger (no), the CPU 40 jumps to StepS73.

[Step S72] The CPU 40 executes the first combined processing, combiningdecision making subroutines A1 to F1.

[Step S73] The CPU 40 determines whether the slice thickness of the CTimage 20 is not larger than the predetermined value Sa and not smallerthan the predetermined value Sb, i.e., within a predetermined range. Ifthe CPU 40 determines that the slice thickness is within thepredetermined range (yes), the CPU 40 proceeds to Step S74; whereas, ifthe CPU 40 determines that the slice thickness is not within thepredetermined range (no), the CPU 40 jumps to Step S75.

[Step S74] The CPU 40 executes the second combined processing made up ofa combination of the decision making subroutines A2 to D2. In thissecond combined processing, decision making subroutines corresponding tothe decision making subroutines E1 and F1 are not executed. The decisionmaking subroutines E1 and F1 constitute processing for determiningwhether a shadow is a shadow corresponding to a blood vessel; and, inthe case of small slick thickness, a shadow corresponding to a bloodvessel diminishes greatly and the decision making subroutines E1 and F1are unable to recognize such shadows. Accordingly, in this step, thedecision making subroutines are not executed. Incidentally, it goeswithout saying that the CPU 40 may execute the decision makingsubroutines E2 and F2 similarly to Step S72.

[Step S75] The CPU 40 determines whether the slice thickness of the CTimage 20 is smaller than the predetermined value Sb. If the CPU 40determines that the slice thickness is smaller (yes), the CPU 40proceeds to the next step S76; whereas, if the CPU 40 determines thatthe slice thickness is not smaller (no), the CPU 40 brings theprocessing to an end and proceeds to Step S44 or S46 of FIG. 10.

[Step S76] The CPU 40 executes the third combined processing made up ofa combination of decision making subroutines A3 to D3. In this thirdcombined processing, decision making subroutines corresponding to thedecision making subroutines E1 and F1 are not executed. Similarly to thecase of the above-described step, since the decision making subroutinesE1 and F1 constitute processing for determining whether a shadow is ashadow corresponding to a blood vessel, decision making subroutinesrelated to the decision making subroutines E1 and F1 are not executed.Incidentally, it goes without saying that the CPU 40 may execute thedecision making subroutines E3 and F3 similarly to the case of Step S72.

The respective decision making subroutines A1 to A3 that are executed inSteps S72, S74 and S76 of FIG. 11 will be described below. FIG. 12 is aflowchart showing details of each of the decision making subroutines A1to A3. FIGS. 13 a and 13 b are provided to conceptually show the mannerof processing each of these decision making subroutines A1 to A3, inwhich FIG. 13( a) is a diagram showing the manner in which imaginaryloops are set on a CT image, while FIG. 13( b) is a graph showing thepixel values of each of the imaginary loops. In general, although thereare some exceptions, the correlation of densities of a shadow located ona loop of radius r from the central position of the shadow exhibits atendency to become larger in a focus shadow than in a blood vesselcross-sectional shadow. To use this tendency in shadow decisionprocessing, the correlation of density variations of a shadow betweenadjacent loops spaced a small radius dr apart from each other is found.Namely, it is known that the density of a shadow greatly differs betweenthe vicinity of the center of the shadow and the vicinity of theperiphery thereof, so that if the correlation of density values onlybetween adjacent loops is found, it is impossible to find an accuratecorrelation between both. In this embodiment, the correlation of densityvariations of the shadow is found. Incidentally, the vicinity of thecentral position of the shadow is determined on the basis of themulti-valued image (b1) of FIG. 3, and the determined center of theshadow is applied to the CT image 20 of FIG. 3, as seen at (a1), andthen the density on each loop is found. Incidentally, in the case of theStep S83 of FIG. 2 a, multi-valued information indicative of amulti-valued image other than a binary image is used in place of the CTimage 20. Details of these decision making subroutines A1 to A3 will bedescribed below in the order of the steps thereof.

[Step S1] The CPU 40 sets a radius r at which to search a shadow, as aninitial value. In FIG. 13 a, for example, a value equivalent to about1.5 times a square-shaped small area of approximately 10×10 pixels isset as the radius r.

[Step S2] The CPU 40 rotates at the radius r by one degree at one timeabout the vicinity of the center of the shadow, and it samples andrecords each pixel value in the range of angles θ=0 to 360 degrees.Incidentally, it is preferable to add a constant value for each loop inorder to equalize the density average of each loop.

[Step S3] The CPU 40 sets a radius r+d obtained by adding the radius rto the small radius dr, to obtain the radius r of the next loop. In FIG.13 a, for example, a value equivalent to about 1.2 times theabove-described small area is set as the small radius dr.

[Step S4] The CPU 40 determines whether the radius r is a maximum, and,if the radius r is not a maximum (no), the CPU 40 jumps to Step S2;whereas, if the radius r is a maximum (yes), the CPU 40 proceeds to StepS5. Incidentally, the maximum radius is a radius which is set in advanceaccording to, for example, the size of a focus target to be extractedand the kinds of first to third decision processing.

[Step S5] The CPU 40 performs a computation based on a predetermineddecision formula. For example, as shown in FIG. 13 a, the CPU 40determines the density differences between pixels at a point P (r, θ)and pixels at a point P′ (r′, θ) in the range of 0 to 360 degrees, anddetermines the sum of the absolute values of the density differences.Namely, the CPU 40 finds the correlation of densities between adjacentloops on the basis of the following formula (1):Σ|density difference between pixels at the same angle on adjacentloops|  (1)

As described above, the correlation of shadows between adjacent loops islarger in a focus shadow than in a blood vessel cross-sectional shadow,so that the computation result of the above formula (1) tends to besmaller in a focus shadow than in a blood vessel cross-sectional shadow.This fact can also clearly understood from each curve shown in FIG. 13b. Specifically, the curve (solid line) of a loop=1 and the curve(dotted line) of a loop=2 show similar density variations, and,therefore, it can be said that the correlation therebetween is extremelylarge. On the other hand, the curve (dotted line) of the loop=2 and thecurve (dot-dashed line) of a loop=3 show utterly different densityvariations, and therefore, it can be said that the correlationtherebetween is small. Incidentally, the computing formula for findingthe correlation between adjacent loops is not limited to theabove-described formula (1).

On the other hand, although there are some exceptions, the magnitude ofa shadow variation in the same loop is smaller in a focus shadow than ina blood vessel cross-sectional shadow. To use this fact in shadow adecision, as shown in FIG. 13 a, the CPU 40 determines the densitydifferences between pixels at the point P (r, θ) and pixels at a pointP″ (r, θ″) in the range of 0 to 360 degrees, and determines the sum ofthe absolute values of the density differences. Namely, the CPU 40 findsthe correlation of densities between adjacent pixels on the same loop onthe basis of the following formula (2):Σ|density difference between adjacent pixels on the same loop|  (2)

As described above, although there are some exceptions, the magnitude ofa shadow variation in the same loop is smaller in a focus shadow than ina blood vessel cross-sectional shadow, so that the computation result ofthe above formula (2) tends to be smaller in a focus shadow than in ablood vessel cross-sectional shadow. This fact can also clearlyunderstood from each curve shown in FIG. 13 b. Namely, it can be saidthat the curve (solid line) of the loop=1 and the curve (dotted line) ofthe loop=2 show small density variations, whereas the curve (dot-dashedline) of the loop=3 shows large density variations. Incidentally, acomputing formula for finding the correlation between adjacent pixels onthe same loop is not limited to the above-described formula (2).

In this embodiment, the following formula (3), made up of a combinationof the above formula (1) and the above formula (2), is used:

$\begin{matrix}{{\prod\limits_{i = 1}^{N}\;( {{constant} \times {\overset{359}{\sum\limits_{0}}\;{{A} \cdot {\overset{359}{\sum\limits_{0}}\;{B}}}}} )},} & (3)\end{matrix}$

where A: density difference between pixels at the same angle on adjacentloops i and i+1; and

B: density difference between adjacent pixels on the same loop i.

The computation result of this formula (3) shows a tendency for a focusshadow to be smaller than a blood vessel cross-sectional shadow.Accordingly, it is possible to make a decision to exclude from focuscandidates a shadow whose computation result obtained from the formula(3) is larger than a constant value. Incidentally, a threshold todiscriminate between a focus candidate shadow and a normal shadow (theconstant of the formula (3)) is experimentally found, and the thresholdis previously recorded in the magnetic disk unit 44 and the like and isused when necessary by being read therefrom.

Although in the formula (3), summation is found after the absolutevalues have been found, the following decision formula (4) of findingthe absolute values after having performed the summation may also beused:

$\begin{matrix}{\prod\limits_{i = 1}^{N}\;{( {{constant} \times {{{\overset{359}{\sum\limits_{0}}A}} \cdot {{\overset{359}{\sum\limits_{0}}B}}}} ).}} & (4)\end{matrix}$

[Step S6] The CPU 40 determines whether the computation result obtainedfrom the decision formula (3) or (4) is larger than a constant value. Ifthe CPU 40 determines that the computation result is larger (yes), theCPU 40 proceeds to Step S7; whereas, if the CPU 40 determines that thecomputation result is not larger (no), the CPU 40 brings the processingto an end and proceeds to the next subroutines B1 to B3.

[Step S7] The CPU 40 excludes from focus candidate shadows the result ofthe decision in Step S6, i.e., a shadow whose computation resultobtained from the decision formula (3) or (4) is determined to be largerthan the constant value (computation result>constant value), andproceeds to the next subroutines B1 to B3.

In the above description of the decision making subroutines A1 to A3,reference has been made to the case where the center of a shadow isdetermined on the basis of the multi-valued (binary) image (b1) shown inFIG. 3 and the center of the shadow is applied to the CT image 20,thereby finding densities on a plurality of concentric loops about thecenter to make a decision as to whether the shadow is a focus candidateshadow. This decision method is merely one example, and various othermodifications are available. Some modifications will be described below.

FIGS. 14 a, 14 b and 15 a, 15 b are views showing the concepts ofseveral methods for searching for a pixel whose density is to be found,spirally from the central position of a shadow. As shown in FIG. 14 a, acorresponding pixel for finding the density of a shadow is identified byselecting the vicinity of the center of a shadow 2 as a reference pointand making a spiral search along loops L1, L2 and L3, which rotateclockwise from the reference point toward the edge of the shadow 2.Namely, the CPU 40 makes a search in the order indicated by solid-linearrows along the first loop L1; then it makes search in the orderindicated by dotted-line arrows along the second loop L2; andsubsequently it makes search in a similar manner along the third loop L3and the following loops (not shown), thereby identifying thecorresponding pixel. Incidentally, the starting point of the search andthe direction of the search are not limited; and, as shown in FIG. 14 b,a search may be concentrically made along the loops L1, L2 and L3 fromthe edge of the shadow 2 toward the vicinity of the center of the shadow2. In addition, the search order of one loop is not limited; and, asshown in FIG. 15 a, as to the loop L2, a search may also be made in theorder of Df→Ff→Fb→Db and then in the order of Cf→Bf→Bb→Cb. Incidentally,the capital alphabetical letters indicative of the above-describedsearch order indicate the y coordinate of the CT image 20, and the smallalphabetical letters indicate the x coordinate of the same. Furthermore,as shown in FIG. 15 b, while a radius is rotationally continuouslyincreased, passage points may also be used as search point pixels.

FIGS. 16( a-1) to 16(c-2) conceptually show sampling points in thesearch methods shown in FIGS. 14 a, 14 b and 15 a, 15 b, show data atthe sampling points in the pattern of values “0” and “1”. Specifically,in each of FIGS. 16( a-1) to 16(c-1), black dots indicate samplingpoints in each of shadows 3-5, respectively. Incidentally, the shadows3-5 correspond to an binary image of FIG. 16( b-1), and any pixel valueon each of the shadows is “1” and the other pixel values are “0”.Numbers attached to the black dots indicate the order of search. Thedensity values of the pixels on the shadows 3-5 that are sampled inaccordance with this search order are shown as graphs in FIGS. 16( a-2)to 16(c-2). As is apparent from FIGS. 16( a-2) to 16(c-2), the densityvalue of each of the sampling points assumes a pattern made of “0” and“1”. The CPU 40 makes a decision as to whether the shadow is a focuscandidate shadow, on the basis of this pattern. For example, if thestate in which the density value at each sampling point is “1”continuously exists from 1 to 9, as shown in FIG. 16( a-1), the shadowis determined as a focus candidate shadow. If, as shown in FIG. 16(b-2), the state in which the density value at each sampling point is “1”continuously exists from 1 to 8 and the state in which the density valueat each sampling point is “0” continues from 9 to 13, the shadow isdetermined as not being a focus candidate shadow. If, as shown in FIG.16( c-2), the state in which the density value is “1” and the state inwhich the density value is “0” are repeated at short cycles, the shadowis determined as not being a focus candidate shadow. Incidentally, it ispreferable to make this decision with values previously learned on thebasis of actual focus shadows.

A method of converting the shape of a shadow in a multi-valued imageinto the degree of density shall here be explained. FIGS. 17 a and 17 bare diagrams showing the concept of this converting method. As shown inFIG. 17 a, when the case where a point 10 is selected as the startingpoint of a search is compared with the case where a point 12 is selectedas the starting point of a search, the number of loop turns required tosearch for a point indicative of a pixel value of zero is larger in thecase where the point 12 is selected as the starting point of the search.Accordingly, by selecting a value proportional to this number of loopturns as the density of the shadow, it is possible to convert the shadowshape of the multi-valued image into density values. Incidentally, inthe above-described embodiment, the weighted center portion of a shadowis adopted as the starting point of a search (the center of the shadow),but as shown in FIG. 17 b, an intersection point 14 of a curveconnecting dots (the circular dots shown in FIG. 17 b) indicating thecenter of the vertical length lines of a shadow and a curve connectingdots (the triangular dots shown in FIG. 17 b) indicating the center ofthe horizontal length lines of a shadow can also be selected as thecenter of the shadow. The starting point of a search may also be thecenter of a circle inscribed in a shadow in addition to the weightedcenter position of the shadow. In other words, the starting point of asearch needs only to be near the center of the shadow. Each of thedecision making subroutines may also be executed on the basis ofdensities converted in this manner.

In addition, since the surroundings of an area to be extracted can beviewed, the above-described method can also be used to make a decisionas to whether an area to take a value larger than a particular CT valuesurrounds the area to be extracted.

FIG. 18 is a view showing a method of determining whether a shadow is afocus candidate shadow (cancer shadow) or a normal shadow. In the graphshown in FIG. 18, the horizontal axis represents the number of loopturns by which a search is made spirally or concentrically, while thevertical axis represents the average value of the densities at samplingpoints in each loop. The average value of densities is found on thebasis of a multi-valued image as shown in FIGS. 16( a-1) to 16(c-1).Incidentally, such average value may also be found on the basis of theoriginal CT image and be normalized. In general, the density of a shadowtends to be high near the center of the shadow and to become lowertoward the periphery thereof. Accordingly, as shown in FIG. 18, as thenumber of loop turns increases like points n1, n2, n3, n4 . . . , orpoints N1, N2, N3, N4 . . . , the average of densities shows a tendencyto decrease gradually. Incidentally, in FIG. 18, a curve 16 representedby the points n1, n2, n3, n4 . . . relates to a cancer shadow, while acurve 18 represented by the points N1, N2, N3, N4 . . . relates to ablood vessel cross-sectional shadow. As is apparent from this figure,the cancer shadow 16, which is a focus candidate shadow, is smaller thanthe blood vessel cross-sectional shadow 18 in the rate of diminishing asthe number of the loop is higher. Accordingly, a multiplicity of curvedata are measured by using actual cancer shadows in advance, and themeasured results are stored in the magnetic disk unit or the like asreference data in advance, and a decision is made as to whether eachshadow is a focus candidate shadow, on the basis of these referencedata.

FIG. 19 is a view showing one example of a CT image in which a focusshadow and a normal shadow exist. In the case where the CPU 40 is todiscriminate between shadows 16 a, 18 a and 18 b, as shown in FIG. 19,the CPU 40 makes a search spirally from the vicinity of the center ofeach of the shadows 16 a, 18 a and 18 b and finds an average densityvalue for each loop. The CPU 40 compares each data on the averagedensity value with each reference data of FIG. 18, and makes a decisionas to whether the average density value coincides with any one of thereference data. For example, in the case where the data of the averagedensity value of the shadow 16 coincides with the curve 16 of FIG. 18,the shadow 16 is presented to a doctor as a candidate for a cancershadow. The data of the average density value of each of the shadows 18a and 18 b approximately coincides with the curve 18 of FIG. 18, butdoes not coincide with any of the reference data. In this case, theshadows 18 a and 18 b are deleted from focus candidate shadows, and arenot presented to the doctor. Incidentally, the following formula (5) isused to make a decision as to whether the data of the average densitydata coincides with the reference data:Σ|Ni−ni|<constant value.  (5)

When the above formula (5) is satisfied, the two curves are regarded ascoincident with each other. In the formula (5), Ni is an average densityvalue as to actually searched sampling points of a loop number i, and niis an average density value (reference data) in a cancer shadow of theloop number i. The above absolute value may also be raised to the δ-thpower (δ=1 to 2). Incidentally, the decision formula is not limited tothis one, and the following formula (6) may also be used:ΠNi−ni|<constant value.  (6)

Although the above-described example has referred to an examination of aknown cancer shadow, another method may be used in which reference dataon normal shadows of blood vessel cross sections are prepared; and, whena shadow coincides with the reference data, the shadow is excluded fromfocus candidates. Furthermore, it is also possible to discriminatebetween shadows by not only the shape fitting between the number of loopturns and the curve of a graph of an average density value for eachloop, but also the shape fitting with the curve of a graph of loopnumber and maximum value for each loop.

The decision making subroutines B1 to B3 executed in Steps S72, S74 andS76 of FIG. 11 will be described below. FIGS. 20 a and 20 b constitute aflowchart showing details of each of the decision making subroutines B1to B3 for identifying a shadow on the basis of the anisotropy of theshadow. FIGS. 21 a, 21 b and 22 a, 22 b are views conceptually showingthe manner of processing of each of these decision making subroutines B1to B3. FIGS. 21 a, 21 b relate to the case of a focus shadow, and FIGS.22 a, 22 b relate to the case of a blood vessel cross-sectional shadow.Details of these decision making subroutines B1 to B3 will be describedbelow in the order of the steps thereof.

[Step S30] The CPU 40 initializes an angle θ to, for example, θ=0degrees. In each of FIGS. 21 a and 22 a, the angle θ=0 is a line whichis shown by an arrow extending from a central position M of a shadow inthe rightward horizontal direction.

[Step S31] The CPU 40 determines a density distribution Vr of the shadowlocated on an α axis at the angle θ and at a radius r from the center Mof the shadow, and a density distribution V90 r of the shadow located onan β axis at the angle θ+90 degrees and at the radius r.

[Step S32] In the case of FIG. 21 a, since the positions of the radius ron the α axis and the β axis lie inside the shadow, the densitydistributions Vr and V90 r assume an equal value. On the other hand, inthe case of FIG. 22 a, the position of the radius r on the α axis liesoutside the shadow and the position of the radius r on the β axis liesinside the shadow, so that the density distributions Vr and V90 r assumeremarkably different values. Accordingly, the CPU 40 can make acomparison decision as to these values and determines whether the shadowis a focus candidate shadow. Specifically, in this step S32, the CPU 40will substitute the density distribution Vr located at the radius r onthe α axis at the angle θ and the density distribution V90 r located atthe radius r on the β axis at the angle θ+90 degrees into the followingformula (7) to find the anisotropy (or else correlation) of the shadow:

$\begin{matrix}{{90\text{-}{degree}\mspace{14mu}{anisotropy}\mspace{11mu}(\theta)} = {\sum\limits_{r = {r0}}^{r = \max}\;{{{{V90r} - {Vr}}}.}}} & (7)\end{matrix}$

In the above formula (7), after the absolute value has been found,summation processing is performed. A formula such as that which finds anabsolute value after having performed summation processing can also beused as a formula which expresses anisotropy. Although FIG. 21 a showsthe case where the radius r is a positive value, the radius r may alsobe a negative value. A correlation may also be found with respect toonly a radius r1 in each of FIGS. 21 b and 22 b. In this case, since thelengths differ, a correlation of average values is found. Furthermore,the angle θ of the 90-degree anisotropy (θ) found by the above formula(7) may also be varied in the range of 0-360 degrees to find theanisotropy expressed by the following formula (8):

$\begin{matrix}{{anisotropy}\mspace{11mu} = {\sum\limits_{0}^{359}\mspace{11mu}{90\text{-}{degree}\mspace{14mu}{anisotropy}\mspace{11mu}{(\theta).}}}} & (8)\end{matrix}$

Otherwise, the anisotropy expressed by the following formula (9) mayalso be found instead of the above formula (8):

$\begin{matrix}{{anisotropy}\mspace{11mu} = {\prod\limits_{0}^{359}\;{90\text{-}{degree}\mspace{14mu}{anisotropy}\mspace{11mu}{(\theta).}}}} & (9)\end{matrix}$

[Step S33] The CPU 40 determines whether the anisotropy (or correlation)found in Step S32 is larger than the maximum anisotropy (or the minimumcorrelation) found in the past. If the CPU 40 determines that theanisotropy (or correlation) is larger than the maximum anisotropy (orthe minimum correlation) (yes), the CPU 40 proceeds to Step S34;whereas, if the CPU 40 determines that the anisotropy (or correlation)is not larger than the maximum anisotropy (or the minimum correlation)(no), the CPU 40 jumps to Step S35. Incidentally, even in the case wherethis processing is in the initial cycle and the maximum anisotropy (orthe minimum correlation) does not exist, the CPU 40 proceeds to StepS34.

[Step S34] The CPU 40 sets the anisotropy (or correlation) found in StepS33 as the maximum anisotropy (or the minimum correlation), and updatesthe value of the maximum anisotropy (or the minimum correlation).

[Step S35] The CPU 40 adds a small angle δ to the angle θ to set theangle θ to an angle (θ+δ). Incidentally, the small angle δ is, forexample, one degree, but may also be another value.

[Step S36] The CPU 40 determines whether computations on anisotropy (orcorrelation) over all angles θ of 0-360 degrees have been completed. Ifthe CPU 40 determines that the computations have been completed (yes),the CPU 40 proceeds to Step S37; whereas, if the CPU 40 determines thatthe computations have not been completed (no), the CPU 40 returns toStep S31 and repeats similar processing until the angle θ reaches 360degrees.

[Step S37] The CPU 40 determines whether the maximum anisotropy islarger than a predetermined value (the minimum correlation is smallerthan a predetermined value). If the CPU 40 determines that the maximumanisotropy is larger (the minimum correlation is smaller), the CPU 40proceeds to Step S38, whereas if the CPU 40 determines that the maximumanisotropy is not larger (the minimum correlation is not smaller), theCPU 40 proceeds to the next subroutines C1 to C3. Incidentally, adecision comparison constant in each of the steps is a value determinedaccording to the conditions (such as slice thickness and tube current)of photography with an X-ray CT device, and may also be automaticallyselected.

[Step S38] Since it has been determined in Step S37 that the maximumanisotropy is larger than the predetermined value (the minimumcorrelation is smaller than the predetermined value), the CPU 40excludes the shadow from focus candidate shadows, and proceeds to thenext subroutines C1 to C3. In the case where the above-describedanisotropy is calculated on an actual focus shadow, such as a cancershadow, the value of the anisotropy tends to be small (large in the caseof the correlation). Accordingly, shadows other than a focus candidateshadow can be effectively excluded by these decision making subroutinesC1 to C3. Specifically, actual focus shadows in many cases exhibitshapes close to circles, as shown in FIG. 21 a, and as shown in FIG. 21b, the density difference between a density distribution Vr1 of theshadow located at the radius r1 at the angle θ and a densitydistribution V90 r 1 of the shadow located at the radius r1 at the angleθ+90 degrees is comparatively small, and the anisotropy of the shadowtends to be small. On the other hand, blood vessel cross-sectionalshadows are elongated, as shown in FIG. 22 a, and, as shown in FIG. 22b, the density difference between the density distribution Vr1 of theshadow located at the radius r1 at the angle θ and the densitydistribution V90 r 1 of the shadow located at the radius r1 at the angleθ+90 degrees is comparatively large, and the anisotropy of the shadowtends to be large. Accordingly, the CPU 40 can easily discriminatebetween a focus shadow and a blood vessel shadow by a value indicativeof the anisotropy found by the above formula (8) or (9).

Incidentally, although the above formula (7) uses the absolute value ofthe difference between the density distribution V90 r and the densitydistribution Vr, this invention is not limited to this example, and theabsolute value may be raised to the δ-th power (δ=2 to 4). In addition,the correlation is not limited to density distribution, and may also bea correlation of computation results, such as a density gradientcorrelation. In addition, since the computation result of the aboveformula (8) or (9) is in general a large value, the computation resultmay also be multiplied by a predetermined constant, so that it can berounded off to a small value that is easy to handle. Moreover, althougha correlation angle of 90 degrees is an angle most effective in making adecision, this invention is not limited to 90 degrees, and anappropriate angle within the range of 90 degrees±30 degrees may beselected as a correlation angle, so that the anisotropy thereof may befound. Furthermore, the computation result of the above formula (8) or(9) (for example, 90-degree anisotropy) may be used to calculate astandard deviation or the like, and the standard deviation or the likecan also used for decision. This invention may use any formula that canfind a correlation of shadow densities in two directions spacedsubstantially 90 degrees apart, and is not limited to the above formula(8) or (9), and may also use the above formula (7).

The decision making subroutines C1 to C3 executed in Steps S72, S74 andS76 of FIG. 11 will be described below. FIGS. 23 a, 23 b constitute aflowchart showing details of each of the decision making subroutines C1to C3 for identifying a shadow on the basis of the ratio of the longradius to the short radius of the shadow. FIGS. 24 a, 24 b conceptuallyshow the manner of processing of each of these decision makingsubroutines C1 to C3. Although the above-described decision makingsubroutines B1 to B3 identify a shadow by using the anisotropy of eachof shadow density distributions in two directions spaced substantially90 degrees apart, these decision making subroutines C1 to C3 identify ashadow by using the ratio of lengths of a shadow in two directionsspaced substantially 90 degrees apart. Details of these decision makingsubroutines C1 to C3 will be described below in the order of the stepsthereof.

[Step S50] The CPU 40 positions the center of rotation near the centerof a shadow density. Namely, as shown in FIG. 24 a, the CPU 40 sets thecenter of rotation of the α axis and the β axis with radii ofpredetermined lengths to the center M of the shadow. In the case of afocus shadow, since a plurality of peaks generally appear in its densitydistribution, the vicinity of the center of the shadow is set as theaverage position of this plurality of peaks. Incidentally, as describedpreviously, on the basis of a binary-image shadow obtained by binarizingthe shadow, the center-weight position of the shadow may be set as thecentral coordinates, or the position found in FIG. 17 b may also be setas the central coordinates.

[Step S51] The CPU 40 initializes the angle θ to, for example, θ=0degrees, and initializes the maximum ratio of lengths of the shadow intwo directions spaced substantially 90 degrees apart to, for example,“1”. The angle θ=0 is shown by an arrow extending from the center M ofthe shadow in the rightward horizontal direction in FIG. 24 a.

[Step S52] The CPU 40 initializes a radius R to a small radius R0 near0, and initializes both a first recording flag 1 and a second recordingflag 2 to “0”.

Through the next steps S53 to S58, as shown in FIG. 24 a, the CPU 40finds the radius r1 on the α axis extending from the center M, which isthe reference of the shadow, and where angle θ is given different valuesand a radius r2 on the β axis at the angle θ+90 degrees. The respectiveradii r1 and r2 represent the distances from the origin M to theboundary of the shadow, and this boundary is determined by finding theinitial positions where the values of density curves α1 and β1 reachabout T % of the density value at the origin, as shown in FIG. 24 b.Incidentally, the value of T % can be arbitrarily set, and is herein setto T=75%. It goes without saying that CT values or values of amulti-valued image themselves can also be used instead of percent.

[Step S53] The CPU 40 determines whether the first recording flag 1 is“0” and the density value of a pixel at the radius R in the direction ofthe α axis is a predetermined value or less. If the CPU 40 determinesthat the density value is not larger than the predetermined value (Yes),the CPU 40 proceeds to Step S54; whereas, if the CPU 40 determines thatthe density value is larger than the predetermined value (No), the CPU40 jumps to Step S55. Incidentally, this predetermined value is a valueobtained by multiplying the density value of the shadow at the origin bythe above-described value T. Namely, in this step, the CPU 40 determineswhether a pixel at the radius R lies on the boundary (edge) of theshadow.

[Step S54] Since it has been determined in Step S53 that the densityvalue of the pixel at the radius R in the direction of the α axis is notlarger than the predetermined value, this radius R indicates thedistance from the origin M to the boundary of the shadow. Accordingly,the CPU 40 selects this radius R as the radius r1 and sets “1” to thefirst recording flag 1, to define the radius r1.

[Step S55] The CPU 40 determines whether the second recording flag 2 is“0” and whether the density value of a pixel at the radius R in thedirection of the β axis is a predetermined value or less. If the CPU 40determines that the density value is not larger than the predeterminedvalue (Yes), the CPU 40 proceeds to Step S56; whereas, if the CPU 40determines that the density value is larger than the predetermined value(No), the CPU 40 jumps to Step S57.

[Step S56] Since it has been determined in the above step S55 that thedensity value of the pixel at the radius R in the direction of the βaxis is not larger than the predetermined value, this radius R indicatesthe distance from the origin M to the boundary of the shadow.Accordingly, the CPU 40 selects this radius R as the radius r2 and sets“1” to the second recording flag 2, to define the radius r2.

[Step S57] The CPU 40 adds a small distance ε to the radius R to set theradius R to a radius (R+ε). Namely, the CPU 40 performs the processingof increasing the radius R by the small increment ε.

[Step S58] The CPU 40 determines whether the radius R is a predeterminedmaximum value (for example, the maximum radius of a shadow which is adecision target), and, if the CPU 40 determines that the radius R is notthe maximum value (No), the CPU 40 returns to Step S53; whereas, if theCPU 40 determines that the radius R is the maximum value (Yes), the CPU40 returns to Step S59. The CPU 40 can find the radius r1 on the α axisat the angle θ and the radius r2 on the β axis at the angle θ+90 degreesby repeating the processing of Steps S54 to S58. Incidentally, in thedescription of this step, reference has been made to the case where theCPU 40 leaves the loop of Steps S53 to S58 depending on the size of theradius R; however, instead of this step S58, the CPU 40 can be made toleave the loop according to whether both the first recording flag 1 andthe second recording flag 2 are “1”.

[Step S59] The CPU 40 finds the ratio of the radius r1 to the radius r2:r1/r2. In this step, the ratio=r1/r2 is found, but since the ratio ismade to be not smaller than 1, the larger of the radius r1 and theradius r2 is made the numerator and the smaller is made the denominator.

[Step S] The CPU 40 determines whether the ratio found in Step S59 islarger than a maximum ratio, and if the CPU 40 determines that the ratiois larger (Yes), the CPU 40 proceeds to Step S61; while, if the CPU 40determines that the ratio is not larger (No), the CPU 40 jumps to StepS62.

[Step S61] Since the ratio found in Step S59 is larger than the maximumratio, the CPU 40 sets that ratio to be the new maximum ratio.Incidentally, since the value “1” defined as the maximum ratio in StepS51 is set in advance, the initially found ratio is recorded as themaximum ratio.

[Step S62] The CPU 40 adds the small angle δ to the angle θ to set theangle θ to the angle (θ+δ). Incidentally, the small angle δ is, forexample, one degree, but may also be another value.

[Step S63] The CPU 40 determines whether the angle θ in Step S62 hasreached 360 degrees, and if the CPU 40 determines that the angle θ issmaller than 360 degrees (No), the CPU 40 jumps to Step S52; whereas, ifthe CPU 40 determines that the angle θ=360 degrees (Yes), the CPU 40proceeds to Step S64. In this manner, it is possible to find the maximumratio of the radius r1 to the radius r2 where the angle θ is varied from0 to 360 degrees.

[Step S64] The CPU 40 determines whether the maximum ratio is largerthan a predetermined constant, and if the CPU 40 determines that themaximum ratio is larger (Yes), the CPU 40 proceeds to Step S65; whereas,if the CPU 40 determines that the maximum ratio is not larger (No), theCPU 40 proceeds to the next decision making subroutines D1 to D3.

[Step S65] Since it has been determined in Step S64 that the maximumratio is larger than the predetermined constant, the CPU 40 excludes theshadow from focus candidate shadows and proceeds to the next decisionmaking subroutines D1 to D3. In this manner, the CPU 40 varies the angleθ in the range of 0 to 360 degrees, and if the maximum ratio of theradius r1 and the radius r2 obtained at this time is larger than thepredetermined constant, the CPU 40 excludes the shadow from focuscandidate shadows. This is because focus candidates are small inanisotropy and approximately close to circular shapes and the ratios oftheir radii r1 to their radii r2 are in many cases smaller than thepredetermined constant. Incidentally, although each of FIGS. 21 b, 22 band 24 b shows only anisotropy in the positive direction on the α axis,the case of anisotropy in the negative direction is also included inthis invention.

In the above-described embodiment, reference has been made to the casewhere anisotropy (correlation) is found by using shadow densitydistributions in two directions spaced substantially 90 degrees apart orthe ratio of the lengths of shadows in two directions spacedsubstantially 90 degrees apart, but anisotropy may also be found byusing other methods. For example, anisotropy (correlation) may also befound by causing the α axis and the β axis shown in FIG. 22 a to movelike the long hand and the short hand of a clock. In this case, assumingthat the α axis and the β axis are the short hand and the long hand,respectively, while the α axis is making one rotation, the β axis makesseveral hundred rotations. In FIG. 22 a, the α axis and the β axis areshown to be perpendicular to each other, but this angle may be made anarbitrary angle η. First, a density distribution in the direction of theα axis is found by determining the angle θ, then the angle is changed byη to find a density distribution in the direction of the β axis, andsubsequently the anisotropy (correlation) of both density distributionsis found. In the case where both the α axis and the β axis aresuperposed on a blood vessel shadow, the anisotropy is small and pixelvalues along the α axis and β axis are large, and the distribution ofthe pixel values is flat. On the other hand, if the shadow is circular,such features are not observed, and pixel values along the α axis and βaxis are large and their distribution is flat. Accordingly, if a case ofsmall anisotropy is discovered, it can be inferred that the angle η atthis time is the branching angle of a blood vessel. For example, in FIG.22 a, when the angle η is about 180 degrees and about 45 degrees, theanisotropy becomes small, so that the shadow is inferred to be a bloodvessel and can be excluded from focus candidate shadows.

The decision making subroutines D1 to D3 executed in Step S72, S74 andS76 of FIG. 11 will be described below. FIG. 25 is a flowchart showingdetails of each of the decision making subroutines D1 to D3. FIGS. 26 ato 26 d conceptually show the manner of processing of each of thesedecision making subroutines D1 to D3, of which FIG. 26 a is a viewshowing one example of a CT image. FIG. 26 b is a view showing oneexample of a binary image obtained by binarizing this CT image. FIGS. 26c and 26 d are views showing on an enlarged scale a part of this binaryimage, and which show how a decision is made by each of the decisionmaking subroutines D1 to D3. By applying the binary image processingshown in FIGS. 5 and 6 to the CT image shown in FIG. 26 a, shadows 261and 262, which seem to be focus candidates, are extracted in the binaryimage shown in FIG. 26 b. Since these shadows 261 and 262 are the samein size and shape, it is difficult to discriminate between the shadows261 and 262 by means of the above-described decision. However, since theshadow 262 appears on a wall portion of the shadow, the possibility thatthe shadow 262 is a cancer shadow is high. The shadow 261 appears in theinside of the shadow, and the possibility that the shadow 261 is a focuscandidate is low. For this reason, in the decision making subroutines D1to D3, a decision is made as to whether a shadow is located on a wallportion, and, on the basis of the result of this decision, a decision ismade as to whether the shadow is a focus candidate shadow. In the casewhere a shadow exists near a wall, as a rotating radius passes throughthe wall, the pixel values of the shadow that cross the radius sharplyvary, and the resultant high variation appears at two locations ofradii. Accordingly, in the case where the shadow is in contact with thewall, an angle of elevation θ of the radii at these two locations islarger than a reference value, so that the shadow is identified as afocus candidate shadow. On the other hand, in the case where the shadowis inside the wall, the angle of elevation θ of the radius is smallerthan the reference value, so that the shadow is not identified as afocus candidate shadow. Details of these decision making subroutines D1to D3 will be described below step by step.

[Step S91] The CPU 40 sets the radius r used to search a shadow to aninitial value. The radius R in this processing is made to be a valuecorresponding to the size of each shadow. For example, about 1.5 timesthe maximum diameter of a shadow, which is a target is set to be theinitial value of the radius r.

[Step S92] The CPU 40 rotates a moving radius r by about 5 degrees atone time in the range from θ=0 degrees to θ=360 degrees, and findsdensity distributions V0 to V355 of the shadow located at the distance rfrom the center of the shadow and at the respective angles, and appliespredetermined computations on these density distributions V0 to V355.Then, if the moving radius of radius r crosses the boundary of theshadow at two or more radii θ1 and θ2, the CPU 40 finds a maximum angleΘ between radii θ1 and θ2. The radii θ1 and θ2 are determined in thefollowing manner. The CPU 40 subtracts the density distribution V(θ+5)at an angle (θ+5) which is 5 degrees larger than a certain angle θ, froma density distribution Vθ at the angle θ; and, if the difference is apositive value and the absolute value is larger than a predeterminedvalue, the CPU 40 sets the larger angle (θ+5) to be θ1. In FIGS. 26 cand 26 d, a moving radius rθ1 corresponds to the moving radius at theangle θ1. On the other hand, in the case where the CPU 40 subtracts thedensity distribution V(θ+5) from the density distribution Vθ, if thedifference is a negative value and the absolute value is larger than thepredetermined value, the CPU 40 sets the smaller angle θ to be θ2. InFIGS. 26 c and 26 d, a moving radius rθ2 corresponds to the movingradius at the angle θ2. After the angle θ1 and the angle θ2 have beenfound in this manner, the CPU 40 defines, as the maximum angle Θ, theangle formed by the moving radius rθ1 at the angle θ2 in the clockwisedirection relative to the moving radius rθ2 at the angle θ2.Incidentally, this method of finding the maximum angle Θ is merely oneexample, and it goes without saying that the maximum angle Θ can befound by other methods. In addition, although the description has beenmade in connection with the case where the moving radius is rotated 5degrees at one time, this case is not limiting, and it goes withoutsaying that the angle of rotation may be changed in increments rangingfrom 1 degree to 10 degrees.

[Step S93] The CPU 40 determines whether the maximum angle Θ found inStep S93 is larger than a predetermined constant value, and, if the CPU40 determines that the maximum angle Θ is larger (yes), the CPU 40proceeds to the next decision making subroutines E1 to E3; whereas, ifthe CPU 40 determines that the maximum angle Θ is not larger (no), theCPU 40 proceeds to Step S94. The constant value is herein made 90degrees. This is because the maximum angle Θ is near 180 degrees in thecase of a focus shadow which appears on a wall portion. Accordingly, insuch a case, the shadow is left as a focus candidate shadow and issubjected to the processing of the next decision making subroutine.

[Step S94] Since it has been determined by the decision in Step S93 thatthe maximum angle Θ is smaller than the predetermined constant value,the CPU 40 excludes the shadow from focus candidate shadows and proceedsto the next decision making subroutines E1 to E3. In many cases, a focusshadow, such as a cancer shadow, appears on a wall portion, but a normalshadow does not appear on a wall portion. Therefore, through thisprocessing, a shadow which does not exist on a wall portion isefficiently excluded from focus candidate shadows.

The decision making subroutine E1 executed in Step S72 of FIG. 11 willbe described here. FIG. 27 is a flowchart showing details of thedecision making subroutine E1. FIGS. 28 a, 28 b conceptually show themanner of processing of this decision making subroutine E1. FIG. 28 ashows processing for a blood vessel cross-sectional shadow generated byapplying the binary image processing of FIGS. 5 and 6, and FIG. 28 bshows processing for a focus shadow. When the binary image processing ofFIGS. 5 and 6 is applied to a CT image to exclude the shadow of a bloodvessel portion thinner than a predetermined value, the shadow of theremaining portion becomes a shadow which seems to be a focus shadow, asshown in FIG. 28 a. Therefore, the blood vessel cross-sectional shadowshown in FIG. 28 a must be excluded from focus candidates. For thisreason, in this decision making subroutine E1, this blood vesselcross-sectional shadow is extracted and excluded. Namely, to excludesuch a blood vessel cross-sectional shadow from focus candidates, theCPU 40 finds a long diameter and a short diameter of the shadow from theminimum value and the maximum value of the portion of the rotatingstraight line which intersects the shadow, and samples pixel valueswhich are respectively located a predetermined distance outward from theshadow along extensions of the long diameter and the short diameter. Inthe case of the blood vessel cross-sectional shadow, the pixel value onthe extension of the long diameter and the pixel value on the extensionof the short diameter indicate clearly different values. In the case ofthe focus shadow, both pixel values indicate approximately the samevalue. Accordingly, on the basis of this fact, it is possible todetermine whether the shadow is a focus candidate shadow. Details ofthis decision making subroutine E1 will be described below, step bystep.

[Step S101] The CPU 40 finds the angle Θ relative to the minimum movingradius while rotating the moving radius. The CPU 40 positions the middlepoint of the straight line of predetermined length to be close to thecenter of the shadow density, and sets the middle point of the straightline to be the center of rotation and rotates the straight line aboutthe center of rotation by about 1 degree at one time in the range fromθ=0 degrees to θ=360 degrees. During this time, the CPU 40 sequentiallycalculates the length of the portion of the radius which crosses theshadow at each of the angles, and finds an angle Φ of the minimum valueof the moving radius. Incidentally, this processing is performed onshadows of multi-valued images. The incremental angle of rotation is notlimited to 1 degree, and may be other angles.

[Step S102] This processing is performed on a CT image which is not yetsubjected to multi-valued image processing. The CPU 40 finds densityvalues (CT values) v1 and v2 at two points located a predetermineddistance “outR” outward from the shadow along the extension of themoving radius at the angle Φ, and finds density values (CT values) v3and v4 at two points located a predetermined distance outR outward fromthe shadow along the extension of a moving radius perpendicular to themoving radius at the angle Φ.

[Step S103] The CPU 40 substitutes the density values found in theprevious step S102 into the following formula (10) or (11):v3+v4>((v1+v2)+constant),  (10)v3+v4>((v1+v2)×constant)  (11)

If the CPU 40 determines that the formula (10) or (11) is satisfied(yes), the CPU 40 proceeds to Step S104, whereas if the CPU 40determines that the formula (10) or (11) is not satisfied (no), the CPU40 brings the processing to an end and proceeds to the next decisionmaking subroutine F1. In the case of the blood vessel cross-sectionalshadow shown in FIG. 28( a), the density values (CT values) v1 and v2 atthe two points located the predetermined distance outR outward from theshadow along the extension of the moving radius at the angle Φ becomeextremely small values. On the other hand, the density values v3 and v4at the two points located the predetermined distance outR outward fromthe shadow along the extension of the moving radius perpendicular to themoving radius at the angle Φ become comparatively large values, becausethe density values v3 and v4 are located on the blood vesselcross-sectional shadow. Accordingly, in the case of the blood vesselcross-sectional shadow shown in FIG. 28 a, the above-described formula(10) or (11) is satisfied. On the other hand, in the case of the focusshadow shown in FIG. 28 b, since the density values v1 to v4 becomeapproximately the same value, the above-described formula (10) or (11)is not satisfied.

[Step S104] Since it has been determined by the decision in Step S103that the above-described formula (10) or (11) is satisfied, the CPU 40excludes the shadow from focus candidate shadows, and proceeds to thenext subroutine F1. Through this processing, the blood vesselcross-sectional shadow, which is generated by the multi-valued imageprocessing of FIGS. 5 and 6 and seems to be a focus shadow, iseffectively excluded from focus candidate shadows. Incidentally, in theabove-described step S102, the density values v3 and v4 are found at thetwo points located a predetermined distance outR outward from the shadowalong the extension of the moving radius perpendicular to the movingradius at the angle Φ. However, as a result of Step S101, an angle Φrelative to the maximum value of the moving radius may also be used, sothat the density values v3 and v4 may be found at two points located atthe predetermined distance outR outward from the shadow along theextension of a moving radius at that angle Φ. In addition, although inthe above-described embodiment the density values v1 and v2 aredetermined from the moving radius of minimum value, the density valuesv1 and v2 may instead be determined from the moving radius of maximumvalue so that the density values v3 and v4 may be determined from themoving radius perpendicular to the moving radius of maximum value.

The decision making subroutine F1 executed in Step S72 of FIG. 11 willbe described below. FIG. 29 is a flowchart showing details of thedecision making subroutine F1. FIGS. 30 a and 30 b conceptually show themanner of processing of this decision making subroutine F1. FIG. 30 ashows processing for a blood vessel cross-sectional shadow generated byapplying the multi-valued image processing of FIGS. 5 and 6, and FIG. 30b shows the density distribution generated in the course of theprocessing for this blood vessel cross-sectional shadow. Similarly tothe above-described decision making subroutine E1, the decision makingsubroutine F1 performs the processing of extracting and excluding ablood vessel cross-sectional shadow. Details of this decision makingsubroutine E1 will be described below in the order of the steps thereof.

[Step S111] The CPU 40 sets a radius r at which to search a shadow at aninitial value. For example, the radius r is set to about 7 mm in thecase of the first decision processing of FIG. 10 which is performed whenthe size of the shadow is smaller than the predetermined value Ra, orthe radius r is set to about 20 mm in the case of the second decisionprocessing of FIG. 10, which is performed when the size of the shadow isnot smaller than the predetermined value Ra and is smaller than thepredetermined value Rb, or the radius r is set to about 30 mm in thecase of the third decision processing of FIG. 10, which is performedwhen the size of the shadow is larger than the predetermined value Rb.These values are parameter values which can be variously modified.

[Step S112] The CPU 40 rotates at the radius r by increments of onedegree about a point in the vicinity of the center of the shadow, andsamples each pixel value in the range of angles θ=0 to 360 degrees andgenerates a density waveform, as shown in FIG. 30 b. FIG. 30 b shows theresult obtained by sampling density values at the position of the radiusr from the shadow center of the blood vessel cross-sectional shadowshown in FIG. 30 a.

[Step S113] The CPU 40 extracts an angle at which a density peakappears, on the basis of the density waveform generated in Step S113.For example, the CPU 40 differentiates the density waveform, andextracts an angle relative to the case where the differential value is“0”, as an angle at which a density peak appears. In the case of a bloodvessel cross-sectional shadow, since the density near the axis of ablood vessel shows a maximum value, an angle at which a density peakappears and the longitudinal direction of the blood vessel areapproximately coincident with each other. In the case of FIGS. 30 a and30 b, the angles Θ at which density peaks appear are α=10 degrees, β=170degrees and γ=210 degrees.

[Step S114] The CPU 40 sums CT values on the radius r at various angles.In FIG. 30 a=, since density peaks respectively appear at the threeangles α, β and γ, the CPU 40 finds the sums of CT values on the radiusr at the respective angles α, β and γ. The sum of CT values on theradius at the angle α is sum(α), the sum of CT values on the radius atthe angle β is sum(β), and the sum of CT values on the radius at theangle γ is sum(γ).

[Step S115] The CPU 40 finds the bisector of an angle made by adjacentradii, and finds the sum of CT values on the bisector. In the case ofFIG. 30 a, since the angles at which the respective density peaks appearare the three angles α, β and γ, the bisectors of the radii which areadjacent to each other at the respective angles are a bisector αβ formedbetween the angle α and the angle β, a bisector βγ formed between theangle β and the angle γ, and a bisector γα formed between the angle γand the angle α. The CPU 40 finds the sum of CT values on the bisectorαβ, the bisector βγ and the bisector γα. The sum of the CT values on thebisector αβ is sum(αβ), the sum of the of the CT values on the bisectorαβ is sum(αβ), the sum of the CT values on the bisector βγ is sum(βγ),and the sum of the CT values on the bisector γα is sum(γα).

[Step S116] The CPU 40 determines whether each of the sums found in theabove-described Steps S114 and S115 satisfies the followingpredetermined conditions. If the CPU 40 determines that the followingpredetermined conditions are satisfied (yes), the CPU 40 proceeds to thenext step S117; whereas, if the CPU 40 determines that the followingpredetermined conditions are not satisfied (no), the CPU 40 brings theprocessing to an end, and proceeds to Step S73 of FIG. 11. In this step,any one of the following predetermined conditions are used.

(Predetermined Condition 1)sum(αβ)×constant<sum(α),sum(αβ)×constant<sum(β),sum(βγ)×constant<sum(β),sum(βγ)×constant<sum(γ),sum(γα)×constant<sum(γ), andsum(γα)×constant<sum(α).

In the case where all of the above-described condition formulas aresatisfied, the predetermined condition 1 is determined as satisfied.

(Predetermined Condition 2)sum(αβ)+constant<sum(α),sum(αβ)+constant<sum(β),sum(βγ)+constant<sum(β),sum(βγ)+constant<sum(γ),sum(γα)+constant<sum(γ), andsum(γα)+constant<sum(α).

In the case where all of the above-described condition formulas aresatisfied, the predetermined condition 2 is determined as satisfied.

(Predetermined Condition 3)average(α˜β)+constant<sum(α),average(α˜β)+constant<sum(β),average(β˜γ)+constant<sum(β),average(β˜γ)+constant<sum(γ),average(γ˜α)+constant<sum(γ), andaverage(γ˜α)+constant<sum(α).

In the case where all of the above-described condition formulas aresatisfied, the predetermined condition 3 is determined as satisfied. Inthe above condition formulas, the term “average(α˜β)” indicates theaverage value of the CT values contained in a sector from the angle α tothe angle β, and the term “average(α)” indicates the average value ofthe CT values on the radius r at the angle α. Accordingly, in this case,it is necessary to newly add processing for finding the average values.Incidentally, it goes without saying that, in Step S116, the CPU 40 maydetermine whether all of these conditions 1 to 3 are satisfied.

[Step S117] The CPU 40 excludes from focus candidate shadows the shadowwhich has been determined as satisfying the predetermined condition, asa result of the decision of Step S116, brings this processing to an end,and proceeds to Step S73 of FIG. 11.

In the above-described manner, the shadows other than the focuscandidate shadow 22 shown (b1) to (b3) in FIG. 3 are sequentiallydeleted, and only the focus candidate shadow 22 is finally left as shownat (b3) in FIG. 3. This focus candidate shadow 22 is the final selectedimage (FIG. 3 at (b3)), and is combined with the original CT image 20(FIG. 3 at (a1)), so that the final combined image shown in FIG. 3 at(a2) is displayed on the CRT display 48. On the image shown in FIG. 3 at(a2), the focus candidate shadow 22 is enclosed with the circular markerM so that the operator's attention is drawn to the focus candidateshadow 22. Incidentally, only the focus candidate shadow 22 need bedisplayed in color or in a painted-out state. Images which are subjectedto the extraction processing of sequentially excluding non-candidateshadows to select the focus candidate shadow 22 are actually displayedin greater numbers than those shown in FIG. 3 at (b2) and 3 at (b3).FIG. 3 merely shows part of the images displayed actually.

FIG. 31 is a view of a modification of the display picture of FIG. 3,and shows the case where a CT image and images being processed on a bitmemory are displayed in combined form. Specifically, FIG. 3 sequentiallydisplays images which are being subjected to the extraction processingof sequentially excluding non-candidate shadows to select the focuscandidate shadow 22; however, in FIG. 31, the original CT image 20 iscombined with each of images which are being processed as shown in FIG.3 at (b1) to 3 at (b3), and a resultant combined image 26 issequentially displayed. Since the combined image is displayed in thismanner, the operator (doctor) can serially observe the process ofsequentially excluding non-candidate shadows from the CT image 20 andselecting the focus candidate shadow. Incidentally, the method ofextracting and selecting a focus candidate shadow is not limited to thisembodiment; and, as shown in FIG. 32 by way of example, there is also amethod of finding average coordinates a′ and b′ of respective shadows aand b and excluding from focus candidate shadows the shadow a or b ifthe average coordinates a′ or b′ lie outside that shadow (the shadow bin the example of FIG. 32). Also, a Quoit filter, Mahalanobis distance,Euclidean distance and the like may be used to select a focus candidateshadow. In the case where a focus candidate shadow is presented to theoperator (doctor), each image may also be stored in the magnetic diskunit 44 together with information indicative of an undetected image, animage that is impossible to identify, or candidate image so thatundetected images, images that are impossible to identify and candidateimages may be displayed in separate windows, classified as shown in FIG.33, in accordance with an instruction from the operator (doctor). Theabove-described windows respectively display such images, but in therespective windows, image supplementary information for identifying theimages, such as the patient names and the patient IDs of the respectiveimages, may also be displayed in list form. In addition, information foridentifying the nature of focus candidate shadows, such as a positiveindication that shadow is a focus, a nature close to a positive(apparent-positive) and a negative indicating that a focus candidateshadow is not a focus, may also be displayed as image supplementaryinformation. A display example of such image supplementary informationis shown in FIG. 83.

Although images are displayed in each of the above-described windows,image supplementary information for identifying the images, such as thepatient names and the patient IDs of the respective images, may also bedisplayed in list form.

One example of a picture which is displayed on the CRT display 48 in thecase where the processing of each of the above-described shadowdecisions is executed will be described below. FIG. 34 is a view showinga display for setting the parameters required for the processing of eachof the above-described decision making subroutines. Incidentally, thereare optimum parameters depending on the sizes of shadows and the statesof densities of shadows; and, when a “SMALL FOCUS” button, a “LARGEFOCUS” button, a “FROSTED-GLASS FOCUS” button or a “HIGH-DENSITY FOCUS”button is selected by a mouse cursor, the corresponding optimumparameters are set. When the setting of the parameters with the displayof FIG. 34 is completed, a focus candidate extracting and displayingdevice which is not shown performs sequential or parallel processing inaccordance with the main flowchart of FIGS. 2 a, 2 b by using theparameters set for each of the kinds of shadows, and detects a smallfocus, a large focus, a ground-glass focus and a high-density focus, andcombines each detected shadow with a cross-sectional image of a CT imageas shown in FIG. 3 to display the combined image on the CRT display 48.

A display method of indicating a detected shadow portion to whichattention is to be directed by enclosing an area of interest, such as afocus candidate shadow, with a marker (circle) will be described below.As shown in FIG. 35, in the case where focus candidate shadows (notshown) on a medical image 30, such as a CT image, an MRI image and anultrasonic image, are enclosed with circles 31, 32 and 33, if aplurality of focus candidate shadows are close to one another, thecircles overlap and the focus candidate shadows themselves or theperipheries of the focus candidate shadows may become difficult toobserve. In this embodiment, instead of overlapping and displaying theplurality of circles 31, 32 and 33, markers are displayed as anaggregation of circular arcs 34, 35 and 36, as shown in FIG. 36 b.Specifically, in the case where the circles 31, 32 and 33 which arerespectively centered at focus candidate shadows p1, p2 and p3, overlapone another, the circular arcs contained in the overlap are erased andthe circles 31, 32 and 33 are drawn as an aggregation of a plurality ofcircular arcs 34, 35 and 36, as shown in FIG. 36 b. Incidentally, sincefocus candidate shadows are generally small, it is preferable that thediameters of the circles be made about 10 times the diameters of thefocus candidate shadows.

FIG. 37 is a flowchart indicating the processing for erasing the overlapamong the above-described plurality of circles and for generating anaggregation of the plurality of circular arcs. Namely, this flowchartshows details of the processing for the case of drawing the point q3 onthe circle 33 of FIG. 36 a as the point q3 on the circular arc 36 ofFIG. 36 b.

[Step S72] The CPU 40 determines whether the point q3 on the circle 33that is centered at the focus candidate shadow p3 is contained in thecircles 31 and 32 centered at the other focus candidate shadows (thefocus candidate shadows p1 and p2 in FIG. 36 a). If the CPU 40determines that the point q3 is contained (yes), the CPU 40 proceeds toStep S91; whereas, if the CPU 40 determines that the point q3 is notcontained (no), the CPU 40 proceeds to Step S92.

[Step S91] Since the CPU 40 has determined in the previous step S90 thatthe point q3 overlaps the other circles, the CPU 40 brings theprocessing to an end without drawing the point q3.

[Step S92] Since the CPU 40 has determined in Step S90 that the point q3does not overlap the other circles, the CPU 40 draws the point q3 andbrings the processing to an end. In the case where the CPU 40 draws thecircle 33 that is centered at the focus candidate shadow p3, the CPU 40performs the processing of the above-described steps S90 to S92 on allpoints on the circle. If the CPU 40 is to draw the circles 31 and 32that are centered at the focus candidate shadows p1 and p2, the CPU 40performs similar processing.

In addition, in the extraction process of a focus candidate shadow, bytaking the inverse proportion of the results of the decision formulas(1) and (2) of Step S5 of FIG. 12, it is possible to find theprobability that the focus candidate shadow is a focus (focuscertainty). Therefore, when the above-described circuit is to bedisplayed, it is possible to reflect the focus certainty by making thesize of the circles proportional to the focus certainty or the size ofthe shadow. Incidentally, the circle may also be displayed as a thickcircle or a large circuit, or in different colors (in the order of thehighness of the focus certainty like red, yellow and blue), or inflashing manner, in proportion to the focus certainty irrespective ofthe size of the circle. Furthermore, the markers are not limited tocircles, and may also be rectangles or ellipses. In addition, in thecase where a focus itself is extracted, a focus candidate shadow itselfmay be displayed in different colors or in flashing form to attract theoperator's attention, or a buzzer or a sound may also be used to attractthe operator's attention.

A display method for displaying to the operator (doctor) a CT image fromwhich a focus candidate shadow is extracted, together with theabove-described marker, will now be explained. FIG. 38 is a view showingone example of a case where a detection result image, in which a focuscandidate shadow is enclosed with a marker, and a magnified image, inwhich the portion within the marker is displayed on a magnified scale,are displayed in one display at the same time. As is apparent from thefigure, the detection result image is the picture (a2) of FIG. 3 or FIG.31, and this magnified image displays the portion within the marker inthe detection result image in the state of being magnified at apredetermined magnification. By this magnified display, the operator(doctor) can more accurately observe the focus candidate shadow. Thedisplay shown in FIG. 38 is a standard picture which is designed toenable various display modes to be selected by display mode selectingbuttons that are arranged in a column at the left-hand end of thepicture. These display mode selecting buttons are respectively providedas icons assigned to six kinds of mode, i.e., standard display mode,horizontal order display mode, vertical order display mode, distanceorder display mode, spiral order display mode and registration orderdisplay mode. Although not shown, there also exists a button fordisplaying/erasing the marker, but the illustration thereof is omitted.

The standard mode is the mode of displaying images in the order in whichextraction processing for focus candidate shadows is performed on theimages, as shown in FIG. 39; and, if extraction processing is performedin the order of, for example, images a-d, the images a-d are displayedin that order. Switching of pictures is performed by means of thepicture switching buttons shown in FIG. 38 at the bottom right thereof.A picture switching button on which a downward triangle is displayed isa next-picture display button, and a picture switching button on whichan upward triangle is displayed is a previous-picture display button.Incidentally, focus candidate shadows exist in the image c at twolocations, and, in this case, a marker display switching button isdisplayed. In addition, a magnification change button for arbitrarilychanging the display magnification of a magnified picture is alsodisplayed, but the illustration thereof is omitted herein.

The horizontal order display mode is the mode of dividing a CT imageinto 16 parts in the horizontal and vertical directions, as shown inFIG. 40 a, assigning numbers to the 16 parts in order from top left tobottom left, and displaying focus candidate shadows in the ascendingorder of the numbers. Accordingly, in the case of afocus-candidate-shadow-extracted image, as shown in FIG. 39, a focuscandidate shadow is displayed in order from top left to bottom left asshown in FIG. 41. At this time, the image c of FIG. 39 is displayedseparately in the top and bottom sections of FIG. 41.

The distance order display mode is the mode of dividing a CT image into16 parts in the horizontal and vertical directions, as shown in FIG. 40b, assigning numbers to the 16 parts in order from top left to topright, and displaying focus candidate shadows in the ascending order ofthe numbers. Accordingly, in the case of afocus-candidate-shadow-extracted image as shown in FIG. 39, a focuscandidate shadow is displayed in order from top left to top right, asshown in FIG. 42. Incidentally, images having focus candidate shadows atapproximately the same division position like the images a and d aredisplayed in the order of extraction processing.

The distance order display mode is the mode of sequentially accessing animage having a focus candidate shadow at the smallest distance to thefirst displayed focus candidate shadow. The spiral order display mode isthe mode of dividing a CT image into 40 parts in the horizontal andvertical directions, as shown in FIG. 43, sequentially assigning numbersto the 40 parts counterclockwise spirally in order from top left tobottom left, and displaying focus candidate shadows in the ascendingorder of the numbers. The registration order display mode is the mode ofdisplaying images in the order of display registered in advance by anoperator. Incidentally, it goes without saying that the above-describedhorizontal and vertical division number is merely one example and otherdivision numbers may also be adopted. In the case of the spiral orderdisplay mode, although images are displayed in order from the outside,images may also be displayed in order from the vicinity of the centertoward the outside. The direction of the spiral may be either clockwiseor counterclockwise. Furthermore, since the inside of a mark, such as acircle, has a different CT value, the inside of the mark may be set toan area of interest to adjust a display level and a display window andrecalculate and modify a display conversion table, thereby easilyidentifiably displaying the mark.

Incidentally, in the decision making subroutines D1 to D3 of FIG. 25,the CPU 40 makes a decision as to whether a shadow is located on a wallportion, and identifies the shadow on the basis of the decision.However, in the case where a shadow is located on a wall portion and thedistance of the contact of the wall and the shadow with each other islong, the possibility that the shadow is not a focus candidate shadow ishigh. Contrarily, if the distance by which the wall and the shadow arein contact is short, the possibility that the shadow is a focuscandidate shadow is high. For this reason, in a modification of thedecision making subroutines D1 to D3 of FIG. 25, if the CPU 40determines from the processing of the decision making subroutines D1 toD3 that a shadow is located on a wall portion, the CPU 40 determineswhether the length of contact between the shadow and the wall portion issmaller than a predetermined length. If the CPU 40 determines that thelength is smaller, the CPU 40 determines that the shadow is a focuscandidate shadow; whereas, if the CPU 40 determines that the length islarger, the CPU 40 excludes the shadow from focus candidate shadows.

FIGS. 44 a to 44 c show a specific example in which, when the CPU 40determines that a shadow is located on a wall portion, the CPU 40determine whether the shadow is a focus candidate shadow from the lengthof contact between the shadow and the wall portion. As shown in FIG. 44a, in a CT image, shadows 441 and 442 exist in contact with a wallportion. By applying the multi-valued image processing and thepredetermines decision process of FIGS. 5 and 6 to the CT image as shownin FIG. 44 a, the shadows 441 and 442, as well as a shadow 443, all ofwhich seem to be focus candidates, are extracted in a multi-valuedimage, as shown in FIG. 44 b. Both of these shadows 441 and 442 are incontact with the wall portion and the shadow 443 is not in contacttherewith, so that although the shadow 443 is excluded by the decisionmaking subroutine of FIG. 25, the CPU 40 cannot discriminate between theshadows 441 and 442. However, since the distance by which the shadow 442is in contact with the wall portion is smaller than that of the shadow441, the possibility that the shadow 442 is a focus shadow is high.Accordingly, the CPU 40 finds the distances by which the respectiveshadows 441 and 442 are in contact with the wall portion, and determineswhether each of the distances is smaller than a predetermined value, anddetermines on the basis of the result of the decision whether each ofthe shadows is a focus candidate. Therefore, since the distance by whichthe shadow 441 is in contact with the interior wall portion is largerthan the predetermined value, the CPU 40 determines that the shadow 441is not a focus candidate shadow. On the other hand, since the distanceby which the shadow 442 is in contact with the wall portion issubstantially smaller than the predetermined value, the CPU 40determines that the shadow 442 is a focus candidate shadow, and, asshown in FIG. 44 c, only the shadow 442 is finally extracted as a focuscandidate shadow.

Referring to FIG. 45 a, the CPU 40 rotates a radius of predeterminedlength by about one degree at one time about a point in the vicinity ofthe center of a shadow 451 in the range of angle θ from 0 degrees to 360degrees. During this time, the CPU 40 finds the length r at which theradius crosses the edge of the shadow at each angle. On the basis ofthis length r, as shown in FIG. 45 b, the CPU 40 plots a curve againstthe horizontal axis showing the angle θ and against the vertical axisshowing the length r of the radius, and performs Fourier expansion onthe curve. On the basis of this Fourier-expanded result, the CPU 40generates a broken-line graph in which the horizontal axis showsfrequency f and the vertical axis shows the Fourier coefficient C, asshown in FIG. 45 c. On the basis of this broken-line graph, the CPU 40determines whether the shadow is a focus shadow. Namely, in thisbroken-line graph, a Fourier coefficient at a frequency f0 is C0, aFourier coefficient at a frequency f1 is C1, and a Fourier coefficientat a frequency f2 is C2. Therefore, the CPU 40 represents each of theFourier coefficients as Ci and each of the frequencies as Fi, and findsthe absolute value |fi×Ci| of the product of Ci and fi; and, further, itfinds the summation Σ|fi×Ci| of the absolute value |fi×Ci|. The CPU 40divides the summation Σ|fi×Ci| by the absolute value |Ci| of the Fouriercoefficient Ci, to calculate a determined value ff. This determinedvalue ff is expressed by the following expression:ff=Σ|fi×Ci|/Σ|Ci|.

The CPU 40 determines whether the shadow is a focus candidate shadow,according to whether this determined value ff is smaller than apredetermined value. In the case where the shadow is a blood vesselcross-sectional shadow 451, as shown in FIG. 45 a, the Fouriercoefficients of high-order frequency components are large in the Fourierexpansion. In contrast, if the shadow is like a focus candidate shadow,low-order frequency components are contained in large quantities andhigh-order frequency components are small in the Fourier expansion.Accordingly, the CPU 40 can determine on the basis of the value of thedetermined value ff whether the shadow is a focus candidate shadow or ablood vessel cross-sectional shadow. Incidentally, instead of thisdetermined value ff, a particular |fi×Ci| may also be used as adetermined value.

FIGS. 46 a to 46 d show another embodiment of the decision makingsubroutine. In FIG. 46 a, the CPU 40 rotates a radius of predeterminedlength by about one degree at one time about the vicinity of the centerof a shadow 461 in the range of angles from θ=0 degrees to θ=360degrees. During this time, the CPU 40 determines a length of the portionof the radius up to the edge of the shadow at each angle. The minimumvalue of the determined lengths is set as r. Namely, a short radius r ofthe shadow 461 is found. The area (the number of constituent pixels) Sof the shadow 461 is divided by this short radius r. Namely, S/r² isfound. The CPU 40 compares this value with a predetermined value anddetermines whether the shadow is a focus shadow. Namely, as shown inFIG. 46 b, if a shadow 462 is a circle, the value of S/r² is π. As shownin FIG. 46 c, if a shadow 463 is a square, the value of S/r² is 4. Asshown in FIG. 46 d, if a shadow 464 is a rectangle consisting of twosquares, the value of S/r² is 8. Therefore, the shadow 464 having therectangular shape as shown in FIG. 46 d must be excluded from focuscandidate shadows. Accordingly, when the value of S/r² is smaller than8, the shadow is selected as a focus candidate shadow, whereas when thevalue of S/r² is not smaller than 8, the shadow is excluded from focuscandidate shadows. Incidentally, these values are one example; and inactual practice, different numerical values may be used. Incidentally,as the above-described short radius r, a value r obtained by dividingthe area S of a shadow by a maximum radius rm of the shadow may also beused as an effective short radius r.

Incidentally, in the above-described embodiment, the binary imageprocessing of FIGS. 5 and 6 is applied to a CT image to perform theprocessing of excluding the shadow of a blood vessel portion that isthinner than a predetermined value. This processing extracts and deletesa shadow having a predetermined number of pixels or less in thehorizontal (X axis) or vertical (Y axis) direction, and so acomparatively large blood vessel which does not conform to thiscondition is not excluded. Even in the case of the shadow of such ablood vessel portion, an elongated shadow as shown in FIG. 28 a can beeffectively excluded by the decision making subroutine E1 of FIG. 27.However, there is a case where, if a comparatively large focus candidateshadow 470 and blood vessel shadows 471 and 472 overlap, as shown inFIG. 47 a, these blood vessel shadows 471 and 472 cannot be excluded.For this reason, in this embodiment, the blood vessel shadows 471 and472, as shown in FIGS. 47 a, 47 b are removed by cutting through usingthe cutting processing shown in FIG. 48.

First of all, in the first step S481, the CPU 40 applies the binaryimage processing of FIGS. 5 and 6 to a CT image, and removes the shadowof a blood vessel portion that is thinner than a predetermined value.After this processing, in Step S482, the CPU 40 finds a temporaryweighted center position of the shadow shown in FIG. 47 a. The weightedcenter position may be found by using the above-described variousmethods. In Step S483, the CPU 40 finds a minimum length Rmin of amoving radius 473 in the shadow while rotating the moving radius 473 inthe direction of an arrow 476. When the minimum length Rmin is found, inStep S484, the CPU 40 determines, as a cutting length L, a valueobtained by multiplying this minimum length Rmin by a constant.Therefore, the cutting length L=Rmin×constant. When the cutting length Lis found, in Step S485, the CPU 40 executes cutting of the shadows ofthe blood vessel portions on the basis of this cutting length L. Thecutting of the shadows of the blood vessel portions is performed asshown in FIG. 47 b. First, the CPU 40 sets decision areas 474 and 475,each made of the number of pixels corresponding to the cutting length Lin the horizontal (X axis) direction or the vertical (Y axis) direction,and determines whether any of the shadows 470 to 472 is smaller than thedecision areas 474 and 475.

In the case of FIG. 47( b), the number of pixels of the cutting length Lis assumed to be about 12 pixels. In the case of the horizontal decisionarea 474, the pixel value of a pixel x1 is “1” and the pixel value of apixel xc is “0”. Accordingly, this decision area 474 does not become acutting target, and the pixels x1 to xc remain unchanged. On the otherhand, in the case of the vertical decision area 475, since the pixelvalues of a pixel y1 and a pixel yc are both “0”, the shadow 471 locatedin this decision area 475 becomes a cutting target, and the pixel valuesof the pixels y1 to yc are converted to “0”. The CPU 40 executes theabove-described cutting processing around the shadows 470 to 472. Inthis manner, the blood vessel shadows 471 and 472 are cut from theshadows shown in FIG. 47 a, and only the focus candidate shadow 470 isleft. Incidentally, a constant for determining the cutting length L ispreferably in the range of 0.5-1.5. In FIG. 47 b, the case where theconstant is 1 has been described. In addition, the setting of thisconstant can also be arbitrarily changed by clicking a rate settingbutton with a mouse cursor, as shown in FIG. 49 a. Furthermore, as shownin FIG. 49 b, the cutting length may also be found on the basis ofpredetermined function processing with respect to the horizontal axisshowing the minimum length Rmin of a moving radius and the vertical axisshowing the cutting length.

FIGS. 50 a and 50 b show a first modification of the decision makingsubroutine. The decision making subroutine of FIGS. 50 a, 50 b isperformed in place of the decision making subroutine E1 of FIG. 27, thedecision making subroutine F1 of FIG. 29 and each of the above-describeddecision making subroutines, or it is performed in parallel with thesedecision making subroutines. FIG. 50 a is a view showing one example ofthe case where the binary image processing of FIGS. 5 and 6 is appliedto a CT image. FIG. 50 b is a view showing on a magnified scale theportion enclosed with a circle 501 in the image subjected to this binaryimage processing, and it shows how this decision is made. There is amedical image in which a ground glass opacity focus candidate shadow,which is bound to a blood vessel, exists in its binary image, as shownin FIG. 50 b. Since this shadow is, as shown, bound to the blood vessel,the shadow has a shape provided with a multiplicity of projectionstoward its periphery as diagrammatically shown in FIG. 50(B). Therefore,this shadow cannot be easily determined by any of the above-describeddecision making subroutines.

For this reason, the CPU 40 finds a temporary weighted center positionsimilar to the above-described case. The CPU 40 finds the minimum lengthof a moving radius in the shadow, while rotating the moving radius aboutthe weighted center position. When this minimum length is found, the CPU40 draws a circle 502 having a radius determined by adding apredetermined value (a value equivalent to the number of pixels, forexample, 1-5 pixels) to the minimum length. The CPU 40 measures thelengths (run lengths) of circular arcs and the number of the circulararcs in which this circle 502 and the shadow are superposed on eachother. For example, in the case of FIG. 50 b, the respective run lengthsof the circular arcs are found so that the run length of a circular arcp1p2 between an intersection point p1 and an intersection point p2 isdefined as “3”, the run length of a circular arc p3p4 between anintersection point p3 and an intersection point p4 is defined as “1”,and the run length of a circular arc p5p6 between an intersection pointp5 and an intersection point p6 is defined as “2”. After the measurementof the run lengths and the counting of the number of the circular arcshave been completed, the CPU 40 draws another circle 503 having a radiusdetermined by adding another predetermined value to the circle 502, andsimilarly counts the run lengths of circular arcs and the number inwhich this circle 503 and the shadow are superposed on each other. InFIG. 50 b, only intersection points q1 and q2 are shown as theintersection points of the circle 503 and the shadow.

In this manner, the CPU 40 gradually increases the radius of the circleand counts the run lengths of circular arcs and the number thereof.FIGS. 51 a and 51 b show the result of this counting. FIG. 51 aschematically shows a count memory which uses run lengths as itsaddresses, and FIG. 51 b shows characteristic curves plotted against thehorizontal axis representing the run lengths of arcs and the verticalaxis representing the number of arcs, the run lengths and the number ofarcs being the contents of the count memory. In the case of FIG. 51 a,the count memory shows values, such as 15 circular arcs, each having arun length of “1”, and 53 circular arcs, each having a run length of“2”. A characteristic curve which corresponds to these values is acharacteristic curve 511 in FIG. 51 b. Accordingly, in the case wherethe shadow is a focus candidate shadow, since the number of portionsprojecting outward from the shadow is extremely large, the shadowexhibits the feature that an extremely large number of circular arcs ofshort run length exist, and the number of circular arcs of long runlength is small. On the other hand, in the case where the shadow is nota focus candidate shadow, the shadow has a shape as shown by acharacteristic curve 512 and exhibits the feature that circular arcs ofshort run length exist and circular arcs of long run length exist byapproximately the same number, unlike the case of the focus candidateshadow. Accordingly, the CPU 40 can make a decision as to whether theshadow is a focus candidate shadow, by identifying the shadow by usingthe position of a peak (in the run length) of such a characteristiccurve. In addition, the CPU 40 may make a decision as to the shadow byusing the shape of a distribution of the characteristic curve, i.e.,half-value widths 513 and 514 and the like. Furthermore, the CPU 40 mayprovide the value of a run length to the input of a neural network orthe like, and make a decision as to the shadow by using the output ofthe neural network. In addition, although in the above-describedembodiment the circular arc lengths obtained in the case where a focuscandidate shadow and a circle overlap are used, circular arc lengthsobtained where a focus candidate shadow and a circle do not overlap maybe used, and further, a combination of both circular arc lengths mayalso be used. Run lengths may also be found on the basis of whether a CTvalue is larger or smaller than a threshold value or whether a densitygradient is larger or smaller than a threshold value.

Incidentally, although in FIG. 50 a circle is used to count run lengthsand the number of arcs, a closed curve corresponding to the shape of ashadow may be generated in the following manner to count run lengths andthe number thereof on the basis of this closed curve. Namely, in thecase of a shadow 520, as shown in FIG. 52 a, the CPU 40 rotates a radiusof predetermined length by increments of one degree about a point at thevicinity of the center of the shadow 520 in the range of an angle θ from0 degrees to 360 degrees. During this time, the CPU 40 finds a length Rat which the radius crosses the edge of the shadow at each angle.Incidentally, if a plurality of intersection points of the radius andthe edge of the shadow exist, the CPU 40 selects the shortest radius.The CPU 40 plots a curve with the horizontal axis showing the angle θand the vertical axis showing the length R of the radius calculated inthis manner. This curve may be as shown in FIG. 52 b. This curve becomesa curve whose apexes and valleys are alternately repeated. Accordingly,the CPU 40 finds the positions of the respective valleys (the angle θ),and marks them onto the shadow display. The positions of the respectivevalleys are discrete on the shadow. Therefore, the CPU 40 generates aclosed curve 521, as shown in FIG. 52 a, by performing the processing ofinterpolation between each of the valleys by means of a spline curve orthe like. When the closed curve 521 is found, the CPU 40 counts thelengths of short curve segments (run lengths) in portions in each ofwhich this closed circle 521 and the shadow 520 are superposed on eachother, as well as the number of the curve segments, similar to theabove-described case of FIGS. 50 a, 50 b. After the measurement of therun lengths and the counting of the number of curve segments have beencompleted as to the circle 521, the CPU 40 draws a closed curve 522formed by adding a predetermined value dR to the radius R(θ) of theclosed curve 521, and similarly counts the run lengths and the number ofcurve segments in the circle 522. In this manner, the CPU 40 graduallyincreases the predetermined value dR and sequentially counts the runlengths and the number of the curve segments. Accordingly, since a countmemory and characteristic curves similar to those shown in FIGS. 51 a,51 b are obtained, the CPU 40 can make a decision as to whether theshadow is a focus candidate shadow, similar to the above-described case.In addition, although in the above-described embodiment the lengths ofthe curve segments obtained in the case where a focus candidate shadowand a circle overlap are used, the lengths of curve segments obtained inthe case where a focus candidate shadow and a circle do not overlap maybe used, and, further, a combination of both may also be used. Curvesegments may also be found on the basis of whether a CT value is largeror smaller than a threshold value or whether a density gradient islarger or smaller than a threshold value.

In the above description, curve segments of a closed curve are used as afeature by which the decision is made, but a Fourier transform of shadowdensities applied to a closed curve can also be used.

FIGS. 53 and 54 are views showing a second modification of the decisionmaking subroutine. The decision making subroutines of FIGS. 53 a to 53 cand 54 a to 54 c are performed in place of the decision makingsubroutine E1 of FIG. 27, the decision making subroutine F1 of FIG. 29and each of the above-described decision making subroutines, or they areperformed in parallel with these decision making subroutines. FIGS. 53 ato 53 c shows processing to be performed on a blood vesselcross-sectional shadow generated by applying the binary image processingof FIGS. 5 and 6 to a CT image, and FIGS. 54 a to 54 c shows processingto be performed on a focus candidate shadow. There is a case where evenif the binary image processing of FIGS. 5 and 6 is applied to a CT imageto remove the shadow of a blood vessel portion that is thinner than apredetermined value, there may remain a blood vessel cross-sectionalshadow 530 which seems to be a focus shadow, as shown in FIG. 53 a.Accordingly, the blood vessel cross-sectional shadow 530 as shown inFIG. 53 a must be excluded from focus candidates. For this reason, inthis modification of the decision making subroutine, this blood vesselcross-sectional shadow 530 is extracted and excluded. Namely, to excludethe blood vessel cross-sectional shadow 530 from focus candidates,notice is taken of the density distribution in the shadow. Namely, inthe case of the shadow 530 of a blood vessel cross-section, in a liveror the like, the contours of density in the shadow exhibit a simple formhaving one peak, as shown in FIG. 53 a. On the other hand, in the caseof a focus candidate shadow 540, as shown in FIG. 54 a, there appearcomplicated contours having a plurality of peaks in the shadow.Accordingly, as to these shadows, the CPU 40 finds continuous segments(run lengths) along which there are continuously positive or negativevalues of density gradient, obtains a subtraction result, or performs adifferential or other processing between the CT values of one pixel andthe pixel adjacent in the x axis direction from left to right and in they axis direction from top to bottom, as shown in FIGS. 53 b and 53 c andFIGS. 54 a and 54 b. For example, as shown in FIG. 53 b, the CPU 40computes the density gradient of each pixel in order from the left-handside, on a straight area 531 along a shadow in the X axis direction.Assuming that the density of an original image, such as a CT imagecorresponding to the shadow 530 of FIG. 53 b is f[y][x], a densitygradient g[y][x] of each pixel is found by a difference between theprevious and next pixels, as expressed by the following formula:g[y][x]=f[y][x+1]−f[y][x−1].

When the computation of the density gradients in the straight area 531is completed in this manner, the CPU 40 sequentially shifts thisstraight area in the y axis direction to compute density gradients. FIG.53 b diagrammatically shows the density gradients in the X axisdirection in the case of each of the straight areas 531 and 532. FIG. 53c diagrammatically shows the density gradients in the Y axis directionin the case of each of the straight areas 533 and 534. The CPU 40 findsthe positive and negative run length segments in each of the straightlines on the basis of these density gradients. For example, in the caseof the straight area 531, the positive run length is “11” and thenegative run length is “4”. In the case of the straight area 532, thepositive run length is “7” and the negative run length is “7”. In thecase of the straight area 533, the positive run length is “6” and thenegative run length is “6”. In the case of the straight area 532, thepositive run length is “6” and the negative run length is “5”. In thismanner, the CPU 40 counts the positive and negative run lengths over thewhole of the shadow 530. The CPU 40 similarly finds the densitygradients of each straight area of the focus candidate shadow 540 asshown in FIGS. 54 a and 54 b, and counts positive and negative runlengths on the basis of the density gradients. In the case of a straightarea 541 of FIG. 54 a, the positive run lengths are “11” and “2” and thenegative run lengths are “3” and “4”. In the case of a straight area542, the positive run lengths are “5” and “2” and the negative runlengths is “2” and “4”. In the case of a straight area 543, as seen inFIG. 4 b, the positive run lengths are “6”, “1” and “1” and the negativerun lengths are “3”, “2” and “3”. In the case of a straight area 544,the positive run lengths are “5”, “2” and “2” and the negative runlengths are “2”, “2” and “3”. It can be seen from this fact that thefocus candidate shadow has shorter length of each run length segment andgreater number of run length segments.

FIG. 54 c shows the relationship between the length and the number ofrun length segments which are calculated in the shadow 530 a of FIG. 53and the focus candidate shadow 540 of FIG. 54 a. As is apparent from thecurves shown in FIG. 54 c, the case of the focus candidate shadowexhibits a tendency to have a peak in a number of run length segmentswhere the lengths are comparatively small, while the case of the bloodvessel cross-sectional shadow or the like exhibits a tendency to have apeak where the lengths are comparatively large. Accordingly, byutilizing these tendencies, the CPU 40 can efficiently exclude the bloodvessel cross-sectional shadow, as shown in FIGS. 53 a to 53 c.Incidentally, in the description of this modification, reference hasbeen made to the case where a density gradient is found from thedifference between the previous and next pixels, but the density of apixel may be compared with a predetermined threshold value to determinethe density gradient of “+” or “−” according to the magnitude of thedensity. As this threshold value, the average value of densities in theentire range of a shadow may also be used. Incidentally, in theabove-described embodiment, reference has been made to the case wheredensity gradients in the X axis direction and the Y axis direction arecalculated, but density gradients as to one arbitrary direction may befound, or density gradients may also be found as to a plurality ofdirections, such as the X axis direction, the Y axis direction and adirection crossing either of these directions at 45 degrees.

The above description has referred to the case where, in the decisionmaking subroutines D1 to D3 of FIG. 25, a decision is made as to whethera shadow is located on a wall portion; and, further, in FIGS. 44 a to 44c, a decision is made as to whether the shadow is a focus candidateshadow, on the basis of the length of contact between the shadow and thewall portion. However, there is a case where shadows 551 and 552, whichare in contact with a wall portion, exist in a CT image, as shown at (a)in FIG. 55. Since the distance of the boundary where the shadow 551 isin contact with the inside of the wall portion is much larger than apredetermined value, it is determined that the shadow 551 is not a focuscandidate shadow. However, the shadow 552 is not a shadow of a cancer orthe like, but is a cancer-accompanying shadow which is concomitant witha cancer (invagination of a pleural membrane), and thecancer-accompanying shadow is characterized by an elongated shapeperpendicular to the pleural membrane. Therefore, the possibility thatthis shadow 552 is excluded from focus candidate shadows is very high.However, it is known that an indistinct shadow like 553 in the vicinityof a cancer accompanying shadow, for instance, in contact with a tip ofthe shadow 552, as shown at (a) in FIG. 55, is a focus candidate. Thiscancer-accompanying shadow 553 cannot be easily extracted by theabove-described processing. For this reason, in this embodiment, thiscancer-accompanying shadow 552 is extracted, and the cancer-accompanyingshadow 552 is displayed in such a manner as to be enclosed with a largemarker (a circle or an ellipse), thereby indicating that a shadow actinglike a focus exists near the cancer-accompanying shadow 552. In thisembodiment, the cancer-accompanying shadow detection processing shown inFIG. 56 is executed to detect such a cancer-accompanying shadow.

[Step S551] First, since a shadow 550 and the shadows 551 and 552, allof which seem to be focus candidate shadows, are extracted in amulti-valued image, as shown at (b) in FIG. 55, the CPU 40 determineswhether these shadows 550, 551 and 552 are in contact with the wallportion. If the CPU 40 determines that each of the shadows 550, 551 and552 is in contact (yes), the CPU 40 proceeds to the next Step S552;whereas, if the CPU 40 determines that a shadow 550, 551 or 552 is notin contact (no), the CPU 40 proceeds to Step S554 and deletes thecorresponding shadows. Since the shadow 550 is not in contact, theshadow 550 is excluded from focus candidates through the shadow deletionprocessing of Step S554.

[Step S552] Then, the CPU 40 determines the proportion in which each ofthe shadows is in contact with the wall portion of the pleural membrane,i.e., whether each of their contact lengths is smaller than apredetermined value. If the CPU 40 determines that each of the contactlengths is smaller (yes), the CPU 40 proceeds to the next Step S553;whereas, if the CPU 40 determines that part of the contact lengths isnot smaller (no), the CPU 40 deletes the corresponding shadow. Since thelength of contact of the shadow 551 with the wall portion is larger thanthe predetermined value, the shadow 551 is deleted through the shadowdeletion processing of Step S554. Since the length of contact of theshadow 552 with the wall portion is much smaller than that of the shadow551, the CPU 40 proceeds to the next Step S553.

[Step S553] The CPU 40 extracts a cancer-accompanying shadow fromcorresponding shadows. Namely, the shadow 442 of FIG. 44 a and theshadow 552 of FIG. 55 a corresponds to shadows, each of which is incontact with the wall portion in a proportion smaller than thepredetermined value. Among such shadows, a cancer-accompanying shadow isa shadow which is too elongated to be determined as a focus candidateshadow. Therefore, the CPU 40 extracts as a cancer-accompanying shadow ashadow which is in contact with the wall portion in a proportion smallerthan the predetermined value and is excluded from focus candidateshadows. Accordingly, as shown at (c) in FIG. 55, the shadow 552 isextracted as a cancer-accompanying shadow. On the other hand, the shadow442 of FIG. 44 a is extracted as a focus candidate shadow.

[Step S554] The CPU 40 determines the shadows which have been determinedas “no” in Step S551 and Step S552.

[Step S555] The CPU 40 displays a large marker 570 (a circle or anellipse) in such a manner as to superpose the marker 570 on the originalimage to enclose the cancer-accompanying shadow detected in Step S553,i.e., a object indicating a focus, as shown in FIG. 57. Incidentally,the size of this marker 570 can be arbitrarily changed by a scalesetting button displayed at the bottom right. The doctor can perform anexamination as to whether a focus shadow exists by visually inspectingthe portion enclosed by this marker 570.

FIGS. 58 a to, 60 b are views showing a third modification of thedecision making subroutine. The decision making subroutine of FIGS. 58 ato 60 b is performed in place of the decision making subroutine E1 ofFIG. 27, the decision making subroutine F1 of FIG. 29 and each of theabove-described decision making subroutines, or it is carried out inparallel with these decision making subroutines. First of all, the CPU40 applies the binary image processing of FIGS. 5 and 6 to a CT imageand finds a temporary weighted center position. The weighted centerposition may be found by using the above-described various methods. TheCPU 40 rotates a radius 581 of predetermined length by increments of 5degrees in the range of angle θ from 0 degrees to 360 degrees about thetemporary weighted center position in the direction of an arrow 583. Theincrement of rotation may be any appropriate value other than 5 degrees.The CPU 40 finds a variance or standard deviation SDθ of CT values of ashadow located on the radius, while rotating the radius.

The result obtained in this manner is plotted in graphs as shown inFIGS. 58 b and 58 c. The variance or standard deviation SDθ of FIG. 58 bis that of the focus candidate shadow 540 shown in FIG. 54 a. Thevariance or standard deviation SDθ of FIG. 58 c corresponds to the bloodvessel cross-sectional shadow 530 shown in FIG. 53 a. The focuscandidate shadow 540 exhibits complicated contours having a plurality ofpeaks, as described previously. The variance or standard deviation SDθassumes various complicated values for the respective angles, as shownin FIG. 58 b, according to the complicated contours. On the other hand,the blood vessel cross-sectional shadow 530 exhibits simple contourshaving one peak. The variance or standard deviation SDθ also assumessimple values which do not greatly vary among the angles, as shown inFIG. 58 c. Accordingly, the CPU 40 can determine whether the shadow is afocus candidate shadow or a blood vessel cross-sectional shadow, on thebasis of the graphs of the variance or standard deviation SDθ, as shownin FIGS. 58 b and 58 c.

Incidentally, the CPU 40 may also find a secondary variance or standarddeviation of the variance or standard deviation SDθ of FIGS. 58 b and 58c to determine whether the shadow is a focus candidate shadow or bloodvessel cross-sectional shadow, according to whether the secondaryvariance or standard deviation is larger or smaller than a predeterminedvalue. In addition, as shown in FIGS. 59 a to 59 c, the CPU 40 maydivide each shadow into areas to find a variance or standard deviationas to each of the divided areas. In the case of FIG. 59 a, the CPU 40divides a shadow into upper and lower shadows along a horizontal linepassing through the middle of the shadow, and finds a variance orstandard deviation SD-U of CT values of the upper shadow and finds avariance or standard deviation SD-D of CT values of the lower shadow;and, further, it finds the secondary variance or standard deviation ofboth as a key feature to be used for making a decision as to the shadow.Incidentally, the CPU 40 may also use as a key determining feature thedifference between the variances or standard deviations SD-U and SD-D ofthe upper and lower shadows or the absolute value of the difference. Inthe case of FIG. 59 b, the CPU 40 divides a shadow into four quadrantsalong horizontal and vertical lines passing through the shadow, andfinds variance or standard deviations SD-1 to SD-4 of CT values of eachof the quadrants as a feature quantity to be used for making a decisionas to the shadow. In the case of FIG. 59 c, the CPU 40 divides a shadowinto areas arranged at equal intervals in the vertical direction, andfinds variance or standard deviations SD-1 to SD-7 of CT values of eachof the divided areas; and, further, it finds a secondary variance orstandard deviation of these variance or standard deviations SD-1 to SD-7as a key feature quantity to be used for making a decision as to theshadow. Incidentally, the CPU 40 may divide the shadow into areasarranged at equal intervals in the horizontal direction.

FIGS. 58 a to 58 c and 59 a to 59 c show the case where the CPU 40 usesvariance or standard deviation of the shadow to make a decision as towhether a shadow is a focus candidate shadow or a blood vesselcross-sectional shadow. As shown in FIGS. 60 a and 60 b, the CPU 40 mayalso make a decision as to a shadow by using a variance or standarddeviation of the shadow and a variance or standard deviation of apredetermined area along the periphery of the shadow. Namely, in thecase where the periphery of a shadow is defined by a moving radiuscircle R(θ), as shown in FIGS. 60 a and 60 b, the difference from avariance or standard deviation in the area R(θ) to R(θ)+dR outward by apredetermined distance dR from this moving radius circle R(θ) is foundas a feature quantity, whereby it is possible to discriminate betweenthe blood vessel cross-sectional shadow shown in FIG. 60 a and the focuscandidate shadow shown in FIG. 60 b. In the case of the blood vesselcross-sectional shadow shown in FIG. 60 a, since the area R(θ) toR(θ)+dR traverses the blood vessel cross-sectional shadow, the varianceor standard deviation assumes a comparatively large value. In the caseof the focus candidate shadow shown in FIG. 60 a, since a substantialpart of the area R(θ) to R(θ)+dR traverses a portion where the shadowdoes not exist, the variance or standard deviation assumes acomparatively small value. Accordingly, the CPU 40 finds the differencebetween a variance or standard deviation inside of the shadow and thevariance or standard deviation in the area R(θ) to R(θ)+dR as a keyfeature quantity to be used for discriminating between the focuscandidate and blood vessel shadow. In addition, the CPU 40 may also setthreshold values, respectively, for this key feature and for thevariance or standard deviation of the area R(θ) to R(θ)+dR,respectively, to make such a decision. Furthermore, this featurequantity or the variance or standard deviation of the area R(θ) toR(θ)+dR may be used as an input value for Mahalanobis distance,Euclidean distance, neural networks and the like, and the CPU 40 maymake such a decision by using the obtained result.

In the above-described embodiment, as a display method for the casewhere a CT image having an extracted focus candidate shadow and a markerare displayed for the operator (doctor), reference has been made to amethod of displaying, at the same time, a detection result image inwhich a focus candidate shadow is indicated by a marker, as shown inFIG. 38 and a magnified image which shows the marker portion on anmagnified scale, as well as a method of providing display in variousmodes as shown in FIGS. 39 to 43. In these methods, in the case wherethe operator (doctor) arbitrarily selects a portion desired to bedisplayed, with a mouse cursor or the like, the CPU 40 may also displayfocus candidate shadows in the order of their locations relative to theselected portion, from closest to farthest. Details of this focuscandidate shadow display processing will be described below withreference to FIGS. 61 a, 61 b and 62.

[Step S621] The CPU 40 sequentially displays combined images, each madeup of a marker and a tomographic image as shown in FIG. 39, 41 or 42, inaccordance with a display mode selected by the standard mode selectingbutton on the standard picture of FIG. 38.

[Step S622] The CPU 40 determines whether a mouse has been clicked at anarbitrary position on a combined image being displayed. If the decisionresult is yes, the CPU 40 proceeds to the next Step S623, whereas if thedecision result is no, the CPU 40 returns to Step S621. In Step S621,the display of a combined image according to the display mode iscontinued. Namely, this means that when a mouse pointer 611 lies at apredetermined position on the combined image as shown in FIG. 61 a, adecision is made as to whether the button of the mouse has been clicked.

[Step S623] Since it has been determined in Step S622 that the mouse hasbeen clicked, the CPU 40 determines whether a focus candidate shadowexists within a circle of radius D from the position of the mousepointer. Namely, in the case of FIG. 61 a, a decision is made as towhether a focus candidate shadow exists within a dashed-line circle ofradius D centered at the mouse pointer 611.

[Step S624] Since it has been determined in the decision of Step S623that a focus candidate shadow does not exist within the circle of radiusD centered at the mouse pointer, the CPU 40 displays “No Focus CandidateShadow Exists”. Since a “CONTINUE” button for determining whether thecurrent display is to be continued without modification and an “END”button for bringing display to an end are included in the display, theCPU 40 determines whether this “END” button has been manipulated with aclick. If the decision result is yes (the “END” button has beenclicked), the CPU 40 brings the processing to an end, whereas if thedecision result is no (the “CONTINUE” button has been clicked), the CPU40 returns to Step S621.

[Step S625] Since it has been determined in the decision of Step S623that a focus candidate shadow exists within the circle of radius Dcentered at the mouse pointer, the CPU 40 sequentially displays imagesin which tomographic images of the focus candidate shadow and markersare combined. In the case of FIG. 61 a, the focus candidate shadows ofthe image a and the image c corresponds to the shadow which existswithin the dashed-line circle of radius D. The focus candidate shadowsof the images b and the images c of FIGS. 41 and 42 correspond to theshadow which exists within the dashed-line circle of radius D.Accordingly, as shown in FIG. 61 b, the images of the existing focuscandidate shadows are sequentially displayed in the circle of radius D.In FIG. 61 b, the images b of FIGS. 41 and 42 are displayed. Althoughnot shown, a “NEXT button” is displayed, and when it is clicked, thenext image c is displayed. There is also a special case where the radiusD is set to be larger than such an image, and when a mouse is clickedwith no marker being displayed, all corresponding markers are displayedin response to the click.

[Step S626] The CPU 40 determines whether the mouse has been clicked atan arbitrary position on the combined image being displayed. If thedecision result is yes (if the mouse is clicked), the CPU 40 returns tothe previous Step S623; whereas, if the decision result is no, the CPU40 returns to Step S625. In Step S625, the display of the focuscandidate shadow is continued. When another position is clicked by themouse pointer, similar processing is performed in this step according towhether a focus candidate shadow exists within a circle of radius Dcentered at the mouse pointer. In the case where the same position isclicked, similar to the case where the aforementioned “NEXT button” isclicked, the next image c is displayed. To provide such a display,information on x coordinates and y coordinates indicating whereextracted focus candidate shadows are located on the image is stored ona predetermined memory space.

FIG. 63 a, 63 b and FIG. 64 a, 64 b are views showing a fourthmodification of the decision making subroutine. Each of the decisionmaking subroutines of FIG. 63 a, 63 b and FIG. 64 a, 64 b is performedin place of the decision making subroutine E1 of FIG. 27, the decisionmaking subroutine F1 of FIG. 29 and each of the above-described decisionmaking subroutines, or it is performed in parallel with these decisionmaking subroutines. FIG. 63 a is a view showing a part of a CT image ofthe case where a plurality of needle- or line-shaped shadows 631 to 639,called spicules, appear in the periphery of a malignant cancer shadow630.

The cancer shadow 630 is a shadow of comparatively low density, and itis difficult to identify. In contrast, the shadows 631 to 639 areshadows of high density and are easy to identify, but they have thedisadvantage that they are easily mistaken for shadows of blood vessels.When the binary image processing of FIGS. 5 and 6 is applied to the CTimage including the spicules as shown in FIG. 63 a, the cancer shadow630 of low density is not extracted and only the needle- or line-shapedshadows 631 to 639 are extracted, as shown in FIG. 63 b. There is a casewhere these spicula shadows 631 to 639 are mistaken for the shadow of ablood vessel portion and are excluded. Therefore, it is necessary todetermine whether the shadows 631 to 639 shown in FIG. 63 b are spiculashadows. For this reason, in this embodiment, the CPU 40 discriminatesbetween such spicula shadows and blood vessel shadows; and, in the caseof the spicula shadows, the CPU 40 finds the weighted center position ofthe shadows and displays the weighted center position with a marker.

First, the CPU 40 applies the binary image processing of FIGS. 5 and 6to the CT image and finds a temporary weighted center position of theshadows. The weighted center position may be found by using theabove-described various methods. The CPU 40 rotates a straight lineabout the temporary weighted center position and finds a straight linewhose portion crossing each the shadows is the longest, i.e., the longlength of each of the shadows, and finds the positions (two points) atwhich this long length line crosses the edge of its shadow. FIG. 64 a isa view showing the state where the long diameter of each of the shadows631 to 639 is found and the intersections of the long diameter and eachof the shadows are found. As is apparent from FIG. 64 a, twointersections are found on each of the shadows 631 to 639. Intersections631A and 631B exist on the shadow 631, and intersections 632A and 632Bexist on the shadow 632. Similarly, intersections 633A to 639A and 633Bto 639B exist on the respective shadows 633 to 639.

The CPU 40 arranges the thus-obtained intersections 631A to 639A and631B to 639B on a pixel memory space, as shown in FIG. 64 b, and assignsstraight strips 631 c to 639 c of predetermined width, whichrespectively connect the intersections 631A and 639A to theintersections 631B to 639B, on the pixel memory space cleared to zero.The CPU 40 adds “1” to pixels which correspond to these straight strips631 c to 639 c, for each of the straight strips 631 c to 639 c. Namely,the CPU 40 adds “1” to a pixel memory corresponding to an area throughwhich the strip-shaped straight line 631 c passes, and adds “1” to apixel memory corresponding to an area through which the straight strip632 c passes. Similarly, the CPU 40 adds “1” to a pixel memorycorresponding to each of the areas through which the respective straightstrips 633 c to 639 c pass. In this manner, the values of the respectivepixel memories at locations corresponding the areas through which therespective straight strips 631 c to 639 c pass are increased.

In the case of FIG. 64 b, the straight strips 631 c to 639 cconcentrically pass through a portion enclosed by a circle 640, so thatthe value of the pixel memory of this portion becomes large. Thus, theCPU 40 extracts a portion in which the value of its pixel memory is, forexample, 4 or more, and defines the portion as the weighted centerposition of the spicula shadows. Incidentally, if portions, in each ofwhich the value of its pixel memory is 4 or more, are close to eachother, the portion having the higher value is defined as the weightedcenter position. When this weighted center position is found, the CPU 40displays a marker 641 centered about the weighted center position, asshown in FIG. 63 b. In this manner, a malignant cancer shadow havingspicula shadows easily mistaken for blood vessel shadows can bedisplayed as a focus candidate shadow.

FIGS. 65 and 66 a to 66 f are views showing a modification of each ofthe decision making subroutine E1 of FIG. 27 and the decision makingsubroutine F1 of FIG. 29, and they show a processing method forextracting and excluding a blood vessel cross-sectional shadow. Theprocessing of FIGS. 65 and 66 a to 66 f may be executed in parallel withthe decision making subroutine E1 of FIG. 27 or the decision makingsubroutine F1 of FIG. 29. Normally, there is a case in which a CT imageof a lung portion (lung region) captures part of the blood vessels whichextend so as to spread radially. Accordingly, in this embodiment,recognizing that some blood vessels extend radially, the CPU 40 extractsand excludes such blood vessel cross-sectional shadows. Namely, in thisembodiment, in the case where blood vessels extend radially as shown inFIG. 65, the CPU 40 compares mutually adjacent tomographic images ofpredetermined slice thickness to determine whether the tomographicimages are blood vessel cross-sectional shadows.

As to the blood vessels which extend radially, as shown in FIG. 65,tomographic images respectively corresponding to planes 651 to 653 arephotographed, as shown in FIG. 65. As to the plane 651, blood vesselcross-sectional shadows 651A and 651B are photographed as tomographicimages, as shown in FIG. 66 a. As to the plane 652, blood vesselcross-sectional shadows 652A and 652B are photographed as tomographicimages, as shown in FIG. 66 b. As to the plane 653, blood vesselcross-sectional shadows 653A, 653B and 653C are photographed astomographic images, as shown in FIG. 66 c. In the tomographic images ofthe radially extending blood vessels, the blood vessel cross-sectionalshadows sequentially change in position and size. Accordingly, the CPU40 superposes mutually adjacent tomographic images, and determineswhether the shadows are blood vessel cross-sectional shadows, on thebasis of the manner of the changes in the positions and the sizes of theshadows.

FIG. 66 d shows tomographic images corresponding to the plane 651 andthe plane 652, which are adjacent to each other, i.e., a superpositionof FIG. 66 a and FIG. 66 b. FIG. 66 e shows tomographic imagescorresponding to the plane 652 and the plane 653 which are adjacent toeach other, i.e., a superposition of FIG. 66 b and FIG. 66 c. For easeof illustration, the blood vessel cross-sectional shadows 652A and 652Bof FIG. 66 b are represented by dashed lines in FIGS. 66 d and 66 e. Asshown in FIG. 66 a, the blood vessel cross-sectional shadow 651A and theblood vessel cross-sectional shadow 652A are partly superposed on eachother. In addition, the blood vessel cross-sectional shadow 651B and theblood vessel cross-sectional shadow 652B are partly superposed on eachother. In the case where the shadows of both tomographic images arepartly superposed, both are deleted as blood vessel cross-sectionalshadows indicative of part of the radially extending blood vessels. Inthe case of the blood vessel cross-sectional shadow 652A and the bloodvessel cross-sectional shadow 653A of FIG. 66 e, since they are partlysuperposed, both shadows are deleted as blood vessel cross-sectionalshadows. In the case of the blood vessel cross-sectional shadow 652B andthe blood vessel cross-sectional shadows 653B and 653C of FIG. 66 e, theblood vessel cross-sectional shadows 653B and 653C branch off from theblood vessel cross-sectional shadow 652B, and the blood vesselcross-sectional shadow 653B is partly superposed on the blood vesselcross-sectional shadow 652B, but the blood vessel cross-sectional shadow653C is not partly superposed on the blood vessel cross-sectional shadow652B. Accordingly, in this case, the blood vessel cross-sectional shadow652B and the blood vessel cross-sectional shadow 653B become objects tobe deleted as blood vessel cross-sectional shadows, but the blood vesselcross-sectional shadow 653C does not become an object to be deleted.Incidentally, in the case where the respective blood vesselcross-sectional shadows 652A and 652B are superposed on shadows 661 and662 in proportions more than a predetermined proportion, as shown inFIG. 66 f, all of these shadows are not detected as focus candidateshadows, and become objects for another decision. Incidentally, theseproportions are calculated for the respective shadows, and the area ofsuperposition is divided by the area of each of the superposed shadows,and the larger value is adopted. Namely, in the case of the blood vesselcross-sectional shadow 652A and the shadow 661, the proportion foundwhen the area of their superposition is divided by the area of the bloodvessel cross-sectional shadow 652 and the proportion found when the areaof the superposition is divided by the area of the shadow 661 differfrom each other, and the result of division by the shadow 661 of smallerarea is adopted. Incidentally, the proportion of the superposition canbe arbitrarily set as a parameter.

Incidentally, in X-ray CT devices and the like, there is a case wheredata of projection on an area in all directions cannot be obtained and atomographic image must be reconstructed. This case is called a partialvolume effect. Even in a decision as to whether a shadow due to thispartial volume effect is mistakenly detected, it is effective to find acorrelation of mutually adjacent images. In the case of the partialvolume effect, since a shadow of high density is imaged in adjacentareas, the proportion of pixels which assumes a larger value than apreset particular CT value in the previously detected area and thecorresponding area (the same position) of its adjacent image can be usedfor the decision.

FIGS. 67 a to 67 d and 68 are views showing a fifth modification of thedecision making subroutine. The decision making subroutine of FIGS. 67 ato 67 d and 68 uses three sets of medical images (axial images, sagittalimages and coronal images) which are mutually perpendicular, anddetection is made as to an abnormal shadow in each of the three sets ofmedical images; and, if there is a correlation between positions wheredetected shadows exist, the shadows are displayed or recorded as focuscandidate shadows. FIG. 67 a shows the concept of coaxial planescorresponding to coaxial images. FIG. 67 b shows one example of acoaxial image corresponding to the coaxial planes shown in FIG. 67 a.FIG. 67 c shows sagittal planes corresponding to sagittal images as wellas coronal planes. In FIG. 67 c, the sagittal planes are shown by solidlines, and the coronal planes are shown by dashed lines. FIG. 67 d showsone example of a sagittal image corresponding to the sagittal planeshown in FIG. 67 c. Incidentally, a coronal image corresponding to thecoronal planes is omitted.

The CPU 40 executes the above-described decision making subroutine onthe basis of two kinds of images, the axial image and the sagittalimage. As a result, it is assumed that focus candidate shadows 671 and672 are detected on the axial image of FIG. 67 b and a focus candidateshadow 673 is detected on the sagittal image of FIG. 67 d. FIG. 68 showsthe contents of a memory which stores data, such as position informationas to the detected focus candidate shadows 671 and 673. The data storedin the memory are made up of the total of shadows and information as toeach of the shadows. Information as to the focus candidate shadows 671and 673 is made up, giving the position coordinates (X, Y, Z) of each ofthe shadows and the area and the maximum and minimum lengths of each ofthe shadows, as well as other information. The position coordinates ofthe focus candidate shadow 671 on the axial image are (X1, Y1), and theposition coordinates of the focus candidate shadow 672 on the axialimage are (X2, Y2). In addition, the position coordinates of the focuscandidate shadow 673 on the sagittal plane are (Z1, Y1+δ).

It is assumed here that the z-axis coordinate is not clearly identifiedon the axial plane and the X-axis coordinate is not clearly identifiedon the sagittal plane. In this case, when the position coordinates (X1,Y1) of the focus candidate shadow 671 and the position coordinates (Z1,Y1+δ) of the focus candidate shadow 671 are compared with each other,their Y-axis coordinates are found to approximate each other. The Y-axisdifference between the focus candidate shadow 671 and the focuscandidate shadow 673 is δ. If this difference δ is smaller than apredetermined value Δ, it is determined that both shadows 671 and 673lie at the same position coordinates and are the same focus candidateshadow, and the shadows 671 and 673 are left as a focus candidateshadow. On the other hand, if the difference δ is smaller than thepredetermined value Δ, it is determined that both shadows 671 and 673lie at different position coordinates, and the shadows 671 and 673 aredeleted from focus candidate shadows. Since nothing on the sagittalplane corresponds to the position coordinates (X2, Y2) of the focuscandidate shadow 672, the focus candidate shadow 672 in this case isregarded as false and is deleted. Incidentally, in this embodiment, thedecision is made according to whether the position coordinates ofshadows are the same, but in the case where the position coordinates ofshadows are regarded as the same, a decision as to whether both shadowsare the same may be made on the basis of the “areas”, the “maximumlengths”, the “minimum lengths” and the like of the shadows.

FIG. 69 shows the case where a CT image having an extracted focuscandidate shadow and a marker are displayed to the operator (doctor)together with a focus candidate shadow photographed and extracted in thepast. FIG. 69 a is a view showing one example of a picture whichdisplays focus candidate shadows detected on the basis of a CT imagephotographed the latest, as well as markers. FIG. 69 b shows one exampleof a picture which displays a marker corresponding to a focus candidateshadow photographed and extracted, for example, one year ago in such amanner that the marker is superposed on the image of FIG. 69 a at thesame time. In FIG. 69 b, the marker corresponding to the focus candidateshadow extracted in the past is shown by a square. In addition, a figureor a character indicating that circular markers 691 and 692 denote thelatest photographed objects is displayed at the top left of the picture,while a figure or a character indicating that a square marker 693 denotethe past (previous) photographed object is displayed at the bottom leftof the picture. Features, such as the coordinates and the size of thefocus candidate shadow photographed and extracted in the past, arerecorded on a magnetic disk in the format shown in FIG. 68, and a markercorresponding to the past focus candidate shadow may be displayed on thebasis of these features. For example, in the case where CT images arephotographed and subjected to cancer detection processing at intervalsof one year, when the detection result of this year and the detectionresult of last year are displayed in a superimposed manner, if a shadownewly appears at a location where no shadow existed last year, a doctorcan be made to recognize that the possibility that the shadow is that ofa cancer is high. Incidentally, the previous date of photography and thelike may also be displayed in association with the marker. In addition,if there exist a plurality of dates of photography, the manner ofdisplay, such as the colors, the shapes and the like of markers, mayalso be changed for the respective dates of photography.

FIGS. 70 a, 70 b and 71 a, 71 b are views showing a modification of themanner of display of a marker. In the above description of theembodiment, reference has been made to the case where the shape of amarker is displayed as a circle or an ellipse. In FIG. 70 a, thedirection of a long diameter 701 indicative of the maximum length of afocus candidate shadow 700 and the direction of a long axis 703 of anelliptical marker 702 are made coincident with each other, and theelliptical marker 702 is displayed to surround the focus candidateshadow 700. Since the marker is displayed along the shape of the shadow,the shadow is easy to recognize. Incidentally, the length of the longaxis of the elliptical marker 702 may use the value obtained bymultiplying the long diameter 701 of the focus candidate shadow 700 ofthe focus candidate shadow by a predetermined coefficient, and thelength of the short axis of the elliptical marker 702 may use the valueobtained by multiplying an effective short diameter (the value obtainedby dividing the area of the shadow by the maximum long diameter of theshadow) of the focus candidate shadow by a predetermined coefficient.FIG. 70 b is a view showing one example of the case where an ellipticalmarker is displayed as an aggregation of elliptical arcs 704 and 705,similar to the case where a marker is shown as an aggregation ofcircular arcs, as shown in FIG. 36 b. In this modification, in the casewhere the ellipses 704 and 705, respectively centered at focus candidateshadows p4 and p5, overlap each other, the overlapping portion of theelliptical arcs is deleted to draw the marker as an aggregation of theplurality of elliptical arcs 704 and 705, as shown in FIG. 70 b. If thefocus candidate shadow is only displayed by being enclosed with themarker 711, as shown in FIG. 71 a, there is a case where the location ofthe focus candidate shadow is difficult to identify. For this reason, asshown in FIG. 71 b, in, this embodiment, the contrast of the CT image inthe area enclosed with the marker 711 is emphasized or the CT image issubjected to gamma processing, so that the focus candidate shadow can bedisplayed in a far more clearly emphasized state.

FIG. 72 is a view showing a modification in which a CT image having anextracted focus candidate shadow and a CT image having no extractedfocus candidate shadow are sequentially displayed as a kinematic image.In the above description of the embodiment, reference has been made tothe case where a CT image having an extracted focus candidate shadow isdisplayed in various display modes. However, in this embodiment,modification is applied to the manner of display, here displaying in akinematic image, which sequentially displays a CT image at a rate ofapproximately 5 to 14 images/second in the order of photographyirrespective of the presence or absence of an extracted focus candidateshadow. Details of this kinematic image display method will be describedbelow with reference to the flowchart of FIG. 72.

[Step S721] The CPU 40 displays the first CT image.

[Step S722] The CPU 40 determines whether an abnormal portion, i.e., afocus candidate shadow, exists in the CT image which is presentlydisplayed. If the decision result is yes, the CPU 40 proceeds to StepS723, whereas, if the decision result is no, the CPU 40 proceeds to StepS724.

[Step S723] Since it has been determined that the abnormal portion(focus candidate shadow) exists in the image which is presentlydisplayed, the CPU 40 increases the delay time. The delay time is thetime required to display one image during kinematic image display. Asthe delay time increases, the display time of the image in which theabnormal portion (focus candidate shadow) exists becomes longer than thestandard display time. Accordingly, the doctor can intensively observewith ample time the image in which the abnormal portion (focus candidateshadow) exists. Incidentally, in the case where a plurality of abnormalportions (focus candidate shadows) exist in one CT image, the value ofthe delay time may be determined according to the number of abnormalportions (focus candidate shadows).

[Step S724] Since it has been established that an abnormal portion(focus candidate shadow) does not exist in the image which is presentlybeing displayed, the CPU 40 decreases the delay time. As the delay timeis decreased, the display of the image comes to an end earlier than thenormal image display. Incidentally, the image may also be displayed witha standard delay time without decreasing the delay time. The value ofthe delay time in each of Step S723 and Step S724 can be arbitrarilyset.

[Step S725] Since the CT image has been displayed for a period of timeequivalent to the delay time, the CPU 40 starts displaying the nextimage.

[Step S726] The CPU 40 determines whether the image displayed in StepS725 is the last image. If the decision result is yes (the image is thelast one), the CPU 40 brings the processing to an end; whereas, if thedecision result is no (the image not the last one), the CPU 40 returnsto Step S722 and repeats the above-described processing until the lastimage is displayed. In the above description, reference has been made tothe case where an image in which no focus candidate shadow exists isdisplayed for only a short time. However, such an image may also bedisplayed for a longer time than the standard display time, so that thedoctor can confirm whether a focus candidate shadow really does notexist.

FIG. 73 is a view showing one example of display processing for the casewhere a diagnosis result provided by the image diagnosis supportingdevice according to this invention is displayed. In the case where adiagnosis is to be made on the basis of a medical image photographedwith a CT device or the like, two doctors independently perform shadowdetection as to the medical image in parallel with each other, andsubsequently meet to examine the results of their shadow detections.Both doctors finally make an integrated decision as to whether the imageis abnormal. If the result of this integrated decision is that the imageis abnormal, the patient undergoes a thorough medical examination. Onthe other hand, if the result of the integrated decision is that theimage is not abnormal, the doctor makes a check as to their diagnosisresult or the like by using an image diagnosis supporting device.Namely, the image diagnosis supporting device must assist in thediagnosis of the doctors and detect the presence or absence of anabnormality in a medical image which has undergone shadow detection bythe doctors. Accordingly, in this embodiment, display processing whichdoes not display a marker after the shadow detection by the doctor hasbeen completed is adopted. Details of this display processing will bedescribed below with reference to the flowchart of FIG. 73.

[Step S731] The CPU 40 directly displays an original CT image in which amarker is not displayed.

[Step S732] The CPU 40 records “completion of displaying” as to the CTimage whose displaying in Step S731 has been completed. For example, aflag indicative of “completion of displaying” is added to the field“other information”, as shown in FIG. 68.

[Step S733] The CPU 40 makes a decision as to whether a display completeicon on the picture has been click-manipulated by a mouse. If thedecision result is yes (the icon is manipulated), the CPU 40 brings thedisplay processing to an end, whereas, if the decision result is no (theicon is not manipulated), the CPU 40 proceeds to Step S734.

[Step S734] The CPU 40 determines whether an icon for displaying amarker (a marker display icon) has been click-manipulated by the mouse.If the decision result is yes (the icon is manipulated), the CPU 40proceeds to Step S735; whereas, if the decision result is no (the iconis manipulated), the CPU 40 returns to Step S733 and repeats theprocessing until the display complete icon or the marker display icon ismanipulated.

[Step S735] The CPU 40 determines whether all CT images have beendisplayed by the processing of Step S731 and “completion of displaying”has been recorded by the processing of Step S732. If the decision resultis yes, the CPU 40 proceeds to Step S737, whereas, if the decisionresult is no, the CPU 40 proceeds to Step S736.

[Step S736] Since it has been determined in the processing of Step S735that all CT images have been displayed, the CPU 40 sequentially displaysimages to which markers are added. During the display of the images towhich markers are added, the CPU 40 may display only marker-added CTimages, each having an extracted focus candidate shadow, in apredetermined order, or it may sequentially display marker-added CTimages and CT images to which no markers are added.

[Step S737] Since it has been in the processing of Step S735 that all CTimages have not been displayed, the CPU 40 displays a notice indicatingthat “there is an image which has not yet undergone shadow detection bythe doctor”, and informs the operator (doctor) that a marker-added CTimage is not displayed, and returns to Step S731. In this manner, shadowdetection by the doctor is performed on a CT image which has not yetundergone shadow detection. In addition, when shadow detection by thedoctor has been performed on a CT image, a marker-added CT image isdisplayed.

FIGS. 74 a to 74 c show another example of display processing for thecase where a diagnosis result provided by the image diagnosis supportingdevice according to this invention is displayed. In the description ofFIG. 73, reference has been made to the case where the presence orabsence of a focus candidate shadow is detected in a medical image whichhas been undergone shadow detection by the doctor and a focus candidateshadow is displayed with a marker added thereto. In the followingdescription, reference has been made to a display method for the casewhere a marker is removed from a marker-added CT image. FIG. 74 a is aview showing one example of a display picture of a marker-added CTimage. In FIG. 74 a, a non-mark icon for selecting the marker-hiddenmode, a next-picture icon for displaying the next picture and an endicon for bringing display to an end are displayed on the left-hand sideof the picture. As shown in FIG. 74 b, when the non-mark icon isclick-manipulated by a mouse, a marker which has been displayed up tothis time disappears and is brought to a hidden state, and thecharacters “NON-MARK MODE IS ACTIVE”, indicative of the hidden state aredisplayed on the bottom side of the display. Furthermore, in thisembodiment, when the non-mark mode of FIG. 74 b passes a predeterminedtime, the picture is restored to the original marker-added CT image, asshown in FIG. 74 c. This predetermined time can be set in advance as aparameter. Incidentally, a mark display icon may be provided on theupper side of the non-mark icon so that the operator can arbitrarilyswitch the non-mark mode and a mark mode. As shown in FIGS. 71 a and 71b, an icon for switching the emphasized display and the non-emphasizeddisplay of a focus candidate shadow may be provided.

In the abnormal shadow detection processing of FIG. 9, reference hasbeen made to the case where processing is performed in parallel infunctional terms, but in sequence in temporal terms according to thekind of shadow, such as a small shadow, a large shadow, a ground glassopacity and a high-density shadow; however, each processing may also beexecuted in parallel by using time division processing or a plurality ofcomputers.

FIG. 75 is a view showing a modification of the main flowchart of theabnormal shadow detection processing of FIG. 9. The abnormal shadowdetection processing of FIG. 75 executes each kind of processingaccording to the kind of shadow in parallel. The ground glass opacity ofStep S751 corresponds to the ground glass opacity processing of FIG. 9.The solid type shadow processing of Step S752 corresponds to three kindsof processing shown in FIG. 9, i.e., the small shadow detectionprocessing, the large shadow detection processing and the high-densityshadow detection processing. The lung-wall-bound type shadow processingof Step S753 corresponds to the decision making subroutines shown inFIGS. 25, 26 a to 26 d and 44 a to 44 c. The specula type shadowprocessing of Step S754 corresponds to the processing shown in FIGS. 63a, 63 b and 64 a, 64 b. The blood-vessel-bound type shadow detectionprocessing of Step S755 corresponds to the processing shown in FIGS. 50to 53. The combining processing of Step S756 combines the resultsextracted by the processing of Steps S751 to S755. These extractedresults include the coordinate position, the area, the maximum lengthand the minimum length of a shadow, as shown in FIG. 68. Accordingly, inthe display storage processing of Step S757, a marker-added CT image,such as the above-described one, is displayed on the basis of theextracted result, and the extracted result is stored in a memory or amagnetic disk.

FIGS. 76 a to 76 d, 77 a and 77 b are views showing a sixth modificationof the decision making subroutine. The decision making subroutine shownFIGS. 76 a to 76 d, 77 a and 77 b is performed in place of the decisionmaking subroutine E1 of FIG. 27, the decision making subroutine F1 ofFIG. 29 and each of the above-described decision making subroutines, orit is performed in parallel with these decision making subroutines. Thisdecision making subroutine uses variance or standard deviation in itsabnormal shadow detection processing. FIGS. 76 a to 76 d show theprinciple of a method of using a variance or standard deviation inabnormal shadow detection processing. FIGS. 77 a and 77 b illustrate oneexample of how to extract a shadow which seems not to be a focus.

It is known that the possibility that a focus candidate shadow close toa circle is a cancer shadow (abnormal shadow) is high, whereas thepossibility that a focus close to a rectangle is a blood vessel shadow(normal shadow) is high. Accordingly, a decision is made by usingstatistical processing according to the shape of a focus candidateshadow. In this statistical processing, the weighted center point of anarea indicative of a focus candidate shadow is found, and the distancefrom the weighted center point to the edge of the area is found alongthe entire periphery of the area. A variance or standard deviation ofthe found distance is computed; and, on the basis of the variance orstandard deviation, it is determined whether the shadow is a focuscandidate shadow. Namely, as shown in FIG. 76 a, in the case where anextracted focus candidate shadow is an approximately circular area,since a distance R from the central point to the edge of the area isequal to a radius R0 of the circle, a variance Da in this case is 0.

As shown in FIG. 76 b, in the case where an extracted focus candidateshadow is a square, for example, the weighted center point of the squarearea is set as the central point, and a circle of radius R0 that iscentered at the central point is drawn. At this time, as shown in FIG.76 b, the length of the radius R0 is made slightly larger than theradius of a circle which touches the inside of the square area, and itis made slightly smaller then a circle which touches the outside of thesquare area. Namely, the length of the radius R0 is set between themaximum value and the minimum value of the distance from the centralpoint to the edge of the shadow. In the case of FIG. 76 b as well, avariance Db is found in a similar manner.

As shown in FIG. 76 c, in the case where an extracted focus candidateshadow is a rectangle, a circle of radius R0 centered at the centralpoint of the rectangular area is drawn. At this time, as shown in FIG.76 c, the length of the radius R0 is made slightly larger than theradius of a circle which touches the shorter sides of the rectangulararea, and is made slightly smaller then a circle which touches thelonger sides of the rectangular area. Namely, in the case of theabove-described square area, the length of the radius R0 is set betweenthe maximum value and the minimum value of the distance from the centralpoint to the edge of the shadow. In the case of FIG. 76 c as well, avariance Dc is found in a similar manner.

The relationship between the variances Db and Dc in the case of each ofFIGS. 76 b and 76 c is Db>Dc. FIG. 76 d is a view showing a specificexample of the case where the above-described principle is applied to anactual blood vessel shadow. A variance D is defined as D=(Σ(R−R0)²)/N.In this formula, R is the distance from the central point of the focuscandidate shadow to the edge of the shadow area. R0 is the length of theradius of the circle centered at the central point, and corresponds tothe average value of a set operation. N is the total number of pixels ofthe focus candidate shadow. S indicates the summation of the distancesto the edge of the focus candidate shadow along the entire periphery ofthe area.

FIGS. 77 a and 77 b show a modification of the case of finding avariance or standard deviation. In FIGS. 77 a and 77 b, variance DX inthe distances LX from one edge to the other edge of a focus candidateshadow along horizontal lines drawn thereon, and then a variance DY inthe distances LY from one edge to the other edge of the focus candidateshadow along vertical lines drawn thereon, are found. Then, on the basisof the relationship in magnitude between each of the variances DX and DYand a predetermined value indicative of the shape of the shadow, adecision is made as to whether the shadow is a cancer shadow, as shownin FIG. 77 a, or a blood vessel shadow, as shown in FIG. 77 b.Incidentally, although the variances may be directly used, it goeswithout saying that the decision can be made by using a standarddeviation which is the square root of each of the variances. A method ofextracting the edge of a focus candidate shadow may use any of a methodusing threshold processing or the like for shadows, a method usingparticular density contours for shaded shadows, and Laplacian processing(refer to MEDICAL IMAGING TECHNOLOGY V01. 16, No. 3, May 1998, pp.209-219), and, further, these methods may also be arbitrarily combined.

FIG. 78 is a view showing a modification of display processing for thecase where a diagnosis result provided by the image diagnosis supportingdevice according to this invention is displayed. FIGS. 79 a to 79 c showone example of a display picture accompanying the display processing ofFIG. 78. As described previously in connection with FIG. 73, in the casewhere a doctor is to make a diagnosis on the basis of a medical imagephotographed with a CT device or the like, if a decision result found bya computer is presented to the doctor before a diagnosis, the diagnosishas the risk of being influenced by preoccupations. Accordingly, it isdesirable that the computer program of this invention not be executedbefore the diagnosis of the doctor. Details of this display processingwill be described below with reference to the flowchart of FIG. 78.

[Step S781] The CPU 40 determines whether an activating button foractivating an execution processing program for an image diagnosissupporting device according to this embodiment has beenclick-manipulated by a mouse. If the decision result is yes (theactivating button is activated), the CPU 40 proceeds to Step S783;whereas, if the decision result is no, the CPU 40 repeats the processingof this step until the activating button is manipulated.

[Step S782] The CPU 40 determines whether there exits a record whoseimage has been displayed on the CRT display 48 (a flag indicative of thecompletion of displaying), and, if the decision result is yes (suchrecord exists), the CPU 40 proceeds to Step S784; whereas, if thedecision result is no (such record exists), the CPU 40 proceeds to StepS783. This is because the record whose image has been displayed on theCRT display 48 is regarded as diagnosed by the doctor. For a far moreexact diagnosis, reference may be made to records of shadow detection bythe doctor, and the decision may be made on the basis of the records. Inthis case, if the CPU 40 determines that there is a record of shadowdetection (yes), the CPU 40 proceeds to Step S784; whereas, if the CPU40 determines that there is not a record of shadow detection (no), theCPU 40 proceeds to Step S783.

[Step S783] Since an image which has not yet been diagnosed by thedoctor is to be displayed, the CPU 40 displays an error message, andreturns to Step S781. As this error message, an error message, as shownin FIG. 79 a or 79 b may be displayed. In FIG. 79 a, an error messageindicating that “error: the program can be activated only after shadowdetection by the doctor” is displayed on the bottom side of atomographic image. In FIG. 79 b, an error message indicating that “note:the program can be activated only when there is a diagnosis record” isdisplayed on the bottom side of the tomographic image.

[Step S784] The CPU 40 activates an execution processing program.

[Step S785] The CPU 40 finds a focus candidate shadow through acomputation on the basis of the execution processing program, anddisplays the result. The CPU 40 records the displayed information on amagnetic disk or the like as required. As shown in FIG. 79 c, thedisplay of the computation result shows the fact that, for example, thefirst tomographic image has one abnormal portion enclosed with a circle,and a value indicating how many abnormal portions have been found intotal inclusive of those in other tomographic images. FIG. 79 c showsthat there are three abnormal portions in total.

FIGS. 80 a to 82 are views showing a seventh modification of thedecision making subroutine. The decision making subroutine of FIGS. 80 ato 82 is performed in place of the decision making subroutine E1 of FIG.27, the decision making subroutine F1 of FIG. 29 and each of theabove-described decision making subroutines, or it is performed inparallel with these decision making subroutines. This decision makingsubroutine uses the area of a shadow and the ratio of areas associatedwith this shadow, in abnormal shadow detection processing. FIGS. 80 a to80 c show one example of the case of finding the area ratio of the totalarea of the entire focus candidate shadow to the area of a concaveportion formed in the edge portion of the shadow. FIGS. 81 a and 81 bshow one example of the process of how a bifurcating portion of a bloodvessel shadow is extracted. FIG. 82 is a flowchart showing one exampleof procedures for the case of finding the area ratio of FIGS. 80 a to 80c. FIG. 83 is a view showing one example of a display which accompaniesthe processing of FIGS. 80 a to 80 c.

An extracted region, as shown in FIG. 81 b, is obtained bydiscriminating between the high and low luminances of a CT image asshown in FIG. 81 a. To identify this extracted region as a blood vesselshadow, this extracted region is binarized by threshold processing. Bythis binarizing processing, a binarized blood vessel region as shown inFIG. 80 a is extracted. FIG. 80 b shows the case where a shadow of acancer or the like is binarized. When the shadow of the cancer or thelike shown in FIG. 80 b is compared with the shadow or the like of theblood vessel region shown in FIG. 80 a, the difference therebetween canbe readily understood, and it can be understood that the shadow of thecancer or the like has a shape close to that of a circle. In the case ofthe blood vessel shadow shown in FIG. 80 a, an area ratio SR of the sumof areas s1 a, s1 b and s1 c of the respective concave portions of theshadow to a total area s2 of the shadow is used to make a decision as toa focus candidate shadow. The area ratio SR may be found by thefollowing ratio formula which simply shows the ratio of the area s1 tothe area s2: SR=s1/s2, or it may also be found by the following ratioformula, which shows the ratio of the total of the area s1 and the areas2 to the area s2: SR=s2/(s1+s2). In the case of the shadow of thecancer or the like shown in FIG. 80 b, an area ratio SR=s10/s20 of anarea 10 s of the concave portion of the shadow to a total area s20 ofthe shadow is used to make a decision as to a focus candidate shadow.FIG. 80 c shows a method of finding the area of the concave portions.Details of the processing of finding the areas of the concave portionswill be described below with reference to the flowchart of FIG. 82.

[Step S821] As shown in FIG. 80 c, the CPU 40 selects a pair of pointsp1 and p2 on the contour of the shadow, and connects both points p1 andp2 by a straight line. The pair is selected only at the first time inthis processing.

[Step S822] The CPU 40 places a point p which moves by a constant lengthat one time from the point p1 toward the point p2, on the straight linewhich connects the two points p1 and p2. Each time the point p moves bythe constant length, the CPU 40 determines whether the point p exists onan extracted region (s2). If the decision result is yes (the p exists onthe extracted region s2), the CPU 40 proceeds to Step S824, whereas, ifthe decision result is no, the CPU 40 proceeds to Step S823.

[Step S823] Since the point p does not exist on the extracted region(s2), the CPU 40 records a particular value (for example, “5”) on thatportion.

[Step S824] The CPU 40 determines whether the point p has completelymoved on the straight line which connects the points p1 and p2, and ifthe decision result is no (the movement of the point p has not yet beencompleted), the CPU 40 returns to Step S822; whereas, if the decisionresult is yes (the movement of the point p has been completed), the CPU40 proceeds to Step S825. By the processing of Step S822 to Step S824,while the point p is moving from the point p1 to the point p2, theparticular value (for example, “5”) is recorded in a region, except forthe extracted region (s2).

[Step S825] In the case where the CPU 40 sets the point p1 as a fixedpoint and the point p2 as a moving point, the CPU 40 determines whetherthe moving point p2 has moved on the entire contour of the extractedregion. In the case where the CPU 40 sets the point p2 as a fixed pointand the point p1 as a moving point, the CPU 40 determines whether themoving point p1 has moved on the entire contour of the extracted region.If the decision result is no (the movement of the moving point has notyet been completed), the CPU 40 returns to Step S821 and performssimilar processing on the next two points. If the decision result isyes, the CPU 40 proceeds to Step S826.

[Step S826] The CPU 40 finds the area (s1) of the extracted region inwhich the particular value (for example, “5”) is recorded. This area s1is the area of the concave portions.

The CPU 40 finds an area ratio RS of the area s1 to the area s2 of theextracted region.

[Step S828] The CPU 40 determines whether the area ratio RS is largerthan a preset constant value. If the decision result is yes (larger),the CPU 40 proceeds to Step S829, whereas, if the decision result is no(smaller or equal), the CPU 40 proceeds to Step S82A.

[Step S829] Since it has been determined in Step S828 that the arearatio RS is larger than the constant value, the possibility that theextracted shadow is a blood vessel shadow is high. Accordingly, the CPU40 deletes the shadow from focus candidate shadows.

[Step S82A] Since it has been determined in Step S828 that the arearatio RS is not larger than the constant value, the possibility that theextracted shadow is a focus candidate shadow is high. Accordingly, theCPU 40 selects the shadow as a focus candidate shadow and savesinformation, such as the coordinate position thereof.

It is preferable to identifiably display the nature of a focus candidateshadow, such as a positive indication that the focus candidate shadow isa focus, a nature close to a positive indication (apparent-positive) ora negative indicating that the focus candidate shadow is not a focus,because the display of the nature supports shadow inspection by adoctor. Accordingly, in the following embodiment, reference will be madeto an image diagnosis supporting device that is capable of readily,instantaneously and identifiably displaying the nature of a shadow,which seems to be an extracted focus candidate.

FIG. 83 is a view showing one example of the case where information foridentifying the nature of a focus candidate shadow on the basis of thearea ratio RS, such as a positive indicating that the focus candidateshadow can be determined as a focus, a nature close to a positive(apparent-positive) and a negative indicating that the focus candidateshadow is not a focus, is displayed as image supplementary information.In the display of FIG. 83, the left-hand window displays a graph inwhich the horizontal axis indicates a CT image having a shadow which isto be judged according to the area ratio RS, and the vertical axiscorresponds to the area ratio RS. The right-hand window displays the CTimage which is the decision target. In the graph, white dots (?) denotethe result of a computation on the extracted shadow, and they aredisplayed at positions corresponding to the magnitude of the area ratioRS. Initially, all marks are displayed as white dots (?) and whether theshadow is positive is in an undetermined state. When a triangle (?) onthe bottom side of the graph is moved by a mouse, an arbitrary white dot(?) can be selected on the graph. The white dot (?) selected by thetriangle (?) changes to a black dot (?). At the same time, a CT imagecorresponding to the selected black dot (?) is displayed on theright-hand window. Accordingly, the operator (doctor) who observes thisCT image determines whether the shadow is positive or negative, and ifthe operator (doctor) determines that the shadow is negative, theoperator (doctor) clicks a “false” icon on the bottom of the CT imagewith the mouse. In this manner, the black dot (?) changes to a square (). On the other hand, if the operator (doctor) determines that theshadow is positive, the operator (doctor) clicks a “positive” icon withthe mouse. In this manner, the black dot (?) changes to an X mark. Atthis time, the highest one of the area ratios RS indicated by X marksbecomes a decision threshold value. On the basis of this decisionthreshold value, a threshold value is determined. The threshold value isa value obtained by multiplying the decision threshold value by aconstant (for example, 1.1). Namely, the threshold=the decisionthreshold value×the constant. Incidentally, the constant may be found byfinding the distribution of squares ( ) and using a standard deviationthereof. Incidentally, in the case where the threshold value isdetermined, a decision as to white dots (?) is made on the basis of thisthreshold value. In this case, squares ( ) or X marks to which whitedots (?) have changed on the basis of the decision with the thresholdvalue may be displayed in different colors or by dotted lines so thatthe operator (doctor) can recognize that a decision as to the white dots(?) has been made on the basis of the threshold value. In thisembodiment, the display as shown in FIG. 83 may also be displayed as animage that is impossible to identify or a focus candidate image as shownin FIG. 33. In this case, the white dots (?) indicative of theundetermined state may be displayed as image supplementary informationon the side of the decision-impossible image, and the X marks determinedas positive may be displayed as image supplementary information on theside of the candidate image.

FIGS. 84 a, 84 b, 85 a and 85 b show a modification of the method offinding the area ratio. First, as shown in FIG. 84 a, a circle isgenerated which is inscribed in the contour of a shadow at three points,and the area ratio RS of the total of areas s1 to s3 of individualregions into which the shadow is divided by the circle to an area s10 ofthe circle is found. This area ratio RS is calculated on the basis ofRS=(s1+s2+s3)/s10. Then, a circle is generated which is circumscribedoutside the shadow at three points, and the area ratio RS of the area s0of the circle to the area s2 of the shadow is found. This area ratio RSis calculated on the basis of RS=s2/s10.

In FIG. 85 a, a polygon (in FIG. 85 a, a triangle) is generated which isinscribed in the adjacent lines of a blood vessel shadow, and the arearatio RS of the total of areas t1 to t3 of individual regions into whichthe shadow is divided by the polygon to an area t0 of the polygon isfound. This area ratio RS is calculated by the formula RS=(t1+t2+t3)/t0.In FIG. 85 b, a polygon (in FIG. 85 b, a pentagon) which is inscribed infocus candidate shadow is generated, and the area ratio RS of the totalof areas t1 to t5 of individual regions into which the shadow is dividedby the polygon to an area T0 of the polygon is found. This area ratio RSis calculated by the formula RS=(t1+t2+t3+t4+t5)/T0. To find the arearatio, in addition to the above-described areas t0 to t3 and T0 to T5,the areas s1 a, s1 b, s1 c and a10 of the respective concave portionsformed outside the shadows may be arbitrarily used.

Furthermore, the concave portions have another feature. Namely, when aconcave portion of a bifurcation of a blood vessel is found, there is acase where three separate regions are obtained. By using this fact, itis possible to delete a blood vessel bifurcation.

In the description of this embodiment, reference has been made to thecase where the contour edge of a binary image is used. However, sincethe contour edge of a binary image corresponds to the isosbestic line ofa multi-valued image shadow, it is possible to perform similarprocessing on the isosbestic line without binarization. Although themagnitude of this key feature quantity may be directly used in adecision, the key feature quantity may be provided as an input signal toa neural network together with other key feature quantities, and theoutput signal from this neural network may also be used in decisionmaking means for selecting a suitable method from among a method using avariance and a method using an area ratio, according to the area of asample to which the method is to be applied; for example, a selectingmenu or the like may be selected with a mouse to improve themanipulability of doctor's diagnosis supporting software.

In addition, by combining a method using a variance and a method usingan area ratio, it is possible to discriminate between a cancer shadowand a blood vessel shadow with far higher accuracy, whereby it ispossible to improve the reliability of the diagnosis supportingsoftware. In addition, in the extraction of a shadow, in the case wherea special shadow, including a shadow of a pleural membrane, is to befound, processing which allows for specialty is needed, as shown inFIGS. 86 a to 86 c. FIGS. 86 a to 86 c show one example of the casewhere a special shadow, including a shadow of a pleural membrane, isfound. In a CT image as shown in FIG. 86, there exist a shadow 861 dueto inflammation or the like, a shadow 552 of a blood vessel or the likeand a shadow 553 perpendicular to the pleural membrane. The CT imageshown in FIG. 86 a is binarized into an image as shown in FIG. 86 b.Furthermore, as shown in FIG. 86 c, when only a contour 865 of thepleural membrane is taken out and the relationship between the shadows861 to 863 and the contour 865 of the pleural membrane is viewed, thenormal blood vessel shadow 861 is deleted on condition that it is not incontact with the contour 865 of the pleural membrane. Furthermore, it ispossible to discriminate between the wide shadow 862 due to inflammationand the shadow 863, which is in perpendicular contact with the contour865 of the pleural membrane, because the wide shadow 862 and the shadow863 differ in the length of contact with the contour 865 of the pleuralmembrane. By designating a particular range of threshold values for theCT image and emphasizing the image, it is possible to extract thecontour 865, as shown in FIG. 86 c. After the contour 865 has beenextracted, the extracted contour 865 is cut and isolated to identifyeach of the blood vessel shadows 861 to 863 on the basis of the state ofconnection of the contour 865 of the pleural membrane with each of theblood vessel shadows 861 to 863. In this manner, it is possible toidentify the kinds of shadows from the relationship between each of theshadows and the contour of the pleural membrane.

In the case where the above-described various feature quantities areused to make a decision as to whether a shadow is a focus candidate,even if processing, such as statistical processing or neural networks,are adopted in an intermediate step, there is a case where it is finallynecessary to determine accurate parameters for thresholding processingor the like. In this case, it goes without saying that it is commonpractice to adopt so-called “learning” in which parameters are made moreaccurate through operations in an inverse direction from normalprocessing, from images obtained every day.

In addition, according to this invention, it is possible to providenovel feature quantities for identifying focus shadows and processingprocedures using such feature quantities.

As described above, according to the image diagnosis supporting deviceof this invention, the above-described various kinds of shadows can behandled in an integrated manner, and so it becomes easy to adjustparameters for improving the discrimination capability, and it alsobecomes easy to create computer programs.

In addition, since a focus candidate shadow can be selected from shadowsby simple processing, the computation time of a computer can be reduced,whereby an accurate focus candidate can be rapidly extracted and anextracted focus candidate can be instantaneously displayed for a doctor.Accordingly, the image diagnosis supporting device is useful in theearly finding of cancers or the like or the inspection of the effect oftreatment.

1. An image diagnosis supporting device characterized by: digitizingmeans for applying predetermined image processing to a medical image andgenerating a multi-valued image comprising discrete multiple values;extracting means for executing at least one decision making processingroutine on the multi-valued image generated by the digitizing means andextracting from among shadows a focus candidate shadow, a shadow whichis likely to indicate a diseased site; and display means for displayingin the medical image the focus candidate shadow extracted by theextracting means so that it is easily identifiable; and image generatingmeans for extracting from the medical image only pixels belonging to apixel value range corresponding to the kind of shadow made by a targetbeing searched and generating a medical image of the target.
 2. An imagediagnosis supporting device according to claim 1, characterized in thatthe extracting means extracts the focus candidate shadow from themulti-valued image generated by the digitizing means and the medicalimage.
 3. An image diagnosis supporting device according to claim 1,further characterized by the digitizing means applying predeterminedmage processing to the target image and generating the multi-valuedimage, the extracting means extracting the focus candidate shadow fromthe multi-valued image and the decision target medical image on thebasis of the multi-valued image generated by the digitizing means.
 4. Animage diagnosis supporting device according to claim 1, furthercharacterized in that the extracting means adjusts a magnification ratioor a reduction ratio of the multi-valued image according to a size of ashadow which is a target, and extracts the focus candidate shadow fromthe multi-valued image that has been so adjusted.
 5. An image diagnosissupporting device according to claim 1, characterized in that theextracting means selects a combination of at least one of the abovedecision making processing routines according to the slice thickness ofthe medical image, and extracts the focus candidate shadow from themulti-valued image through the selected combined processing routines. 6.An image diagnosis supporting device according to claim 1, characterizedin that the decision making means detects a center or a weighted centerof a shadow on the basis of the multi-valued image, rotates on theshadow in the multi-valued image a radius of predetermined length abouta reference point near the center or the weighted center of the shadow,samples pixels of the shadow in the multi-valued image which intersectthe radius as it is rotated, and makes a decision as to whether theshadow is a focus candidate shadow, on the basis of the pixel values. 7.An image diagnosis supporting device according to claim 6, characterizedby sampling the values of pixels forming a spiral or forming concentriccircles as the radius rotates, finding a representative value of each ofloops formed by the rotation on the basis of the individual pixelvalues, comparing the representative value with a reference value storedin advance, and making a decision as to the nature of the shadow.
 8. Animage diagnosis supporting device according to claim 6, characterized bycausing a radius of predetermined length to make rotations on each ofthe plurality of shadows in the multi-valued image about a referencepoint near a detection point of each of the shadows in the multi-valuedimage, sampling pixel values which intersect the radius, checkingcorrelation of the pixel values of mutually adjacent loops formed by therotations, and making a decision as to each of the shadows on the basisof the correlation.
 9. An image diagnosis supporting device according toclaim 1, characterized in that the decision making means detects thecenter or the weighted center of a shadow on the basis of themulti-valued image, rotates two straight lines which are at apredetermined angle η to each other about a reference point near thecenter or the weighted center of a shadow in the multi-valued image, themedical image or the target medical image, samples pixel values of theshadow which intersect the two straight lines, searches for anisotropyof the shadow on the basis of the pixel values corresponding to the twostraight lines, and makes a decision as to whether the shadow is a focuscandidate shadow.
 10. An image diagnosis supporting device according toclaim 1, characterized in that the decision making means detects acenter or a weighted center of a shadow on the basis of the multi-valuedimage, causes a radius of predetermined length to make rotations about areference point near the center or the weighted center of a shadow inthe multi-valued image, samples pixel values of the shadow where itintersects the radius, detects at least two radii at which the pixelvalues sharply change with the rotation and finds the angle ofelevation, the angle between these radii, and compares the angle ofelevation with a reference value stored in advance, and makes a decisionas to whether the shadow is a focus candidate shadow.
 11. An imagediagnosis supporting device according to claim 10, characterized in thatin the case where the decision making means compares the angle ofelevation with the reference value stored in advance and determines thatthe shadow is a focus candidate shadow, the decision making means findsthe length of contact between the shadow and a wall portion anddetermines on the basis of the length of contact whether the shadow is afocus candidate shadow or a shadow of an object accompanying a focus.12. An image diagnosis supporting device according to claim 1,characterized in that the decision making means detects a center or aweighted center of a shadow on the basis of the multi-valued image,rotates a straight line of predetermined length about a reference pointnear the center or the weighted center of a shadow in the multi-valuedimage to find the maximum value or minimum value, or both, of a lengthof the portion of the straight line which intersects the shadow in themulti-valued image, samples first and second pixel values in themulti-valued image which are located a predetermined distance outwardfrom the shadow along an extension of a straight line which passesthrough the reference point and is approximately perpendicular to astraight line with the above minimum value as well as third and fourthpixel values in the multi-valued image which are located a predetermineddistance outward from the shadow along an extension of the straight lineof the minimum value, fifth and sixth pixel values in the multi-valuedimage which are located a predetermined distance outward from the shadowalong an extension of a straight line which passes through the referencepoint and is approximately perpendicular to the straight line with theabove maximum value as well as seventh and eighth pixel values in themulti-valued image which are located a predetermined distance outwardfrom the shadow along an extension of the straight line of the maximumvalue, or ninth to twelfth pixel values in the multi-valued image whichare located a predetermined distance outward from the shadow along theextensions of the straight lines of the maximum value and the minimumvalue, and makes a decision as to whether the shadow is a focuscandidate shadow, on the basis of the first to fourth pixel values, thefifth to eighth pixel values or the ninth to twelfth pixel values. 13.An image diagnosis supporting device according to claim 1, characterizedin that the decision making means detects a center or a weighted centerof a shadow on the basis of the multi-valued image, rotates a radius ofpredetermined length about a reference point near the center or theweighted center of the shadow on the shadow in the multi-valued image tosample pixel values of the shadow which intersects the radius in themulti-valued image, generates a density waveform on the basis of thepixel values, finds the radii of at least two locations corresponding toangles at which peaks of the density waveform appear, finds the bisectorof an angle made by the detected radii, and makes a decision as towhether the shadow is a focus candidate shadow on the basis of the sumof the pixel values on the detected radii and the sum of the pixelvalues on the bisector.
 14. An image diagnosis supporting deviceaccording to claim 1, characterized in that the decision making meansdetects a center or a weighted center of a shadow on the basis of themulti-valued image, rotates a radius of predetermined length about areference point near the center or the weighted center of the shadow onthe shadow in the multi-valued image to sample pixel values of theshadow which intersect the radius, generates a density waveform on thebasis of the pixel values, finds radii lying at least two locations atwhich peaks of the density waveform appear, and makes a decision as towhether the shadow is a focus candidate shadow on the basis of theaverage value of the pixel values on the detected radii and the averagevalue of the pixel values on radii other than the detected radii.
 15. Animage diagnosis supporting device according to claim 1, characterized inthat the decision making means detects a center or a weighted center ofa shadow on the basis of the multi-valued image, rotates a straight lineof predetermined length about a reference point near the center or theweighted center of the shadow on the shadow to find the length of theportion of the straight line which intersects the shadow in themulti-valued image, performs Fourier expansion on a curve describing thelength of the straight line portion with varying angle of rotation, andmakes a decision as to whether the shadow is a focus candidate shadow onthe basis of a result of this Fourier expansion.
 16. An image diagnosissupporting device according to claim 15, characterized by rotating thestraight line of predetermined length on the shadow in the multi-valuedimage, plotting a curve where the vertical axis represents the length ofthe portion of the straight line which intersects the shadow and thehorizontal axis represents the angle, detecting a positions of valleysof the curve, and performing interpolation between the values with apredetermined curve to draw the curve.
 17. An image diagnosis supportingdevice according to claim 1, characterized in that the decision makingmeans detects a center or a weighted center of a shadow on the basis ofthe multi-valued image, rotates a straight line of predetermined lengthabout a reference point near the center or the weighted center of theshadow to find a minimum value of the length of the portion of thestraight line which intersects the shadow, divides the area of theshadow by the second power of the minimum value, and makes a decision asto whether the shadow is a focus candidate shadow, on the basis of thedivided area.
 18. An image diagnosis supporting device according toclaim 1, characterized in that the extracting means detects a center ora weighted center of a shadow on the basis of the multi-valued image,rotates a straight line of predetermined length about a reference pointnear the center or the weighted center of the shadow to find a minimumvalue of length of the portion of the straight line which intersects theshadow, finds a cutting length on the basis of the minimum value, andexcludes an elongated shadow of the cutting length which touches themain shadow.
 19. An image diagnosis supporting device according to claim1, characterized in that the decision making means detects a center or aweighted center of a shadow on the basis of the multi-valued image,draws a closed curve corresponding to a shape of the shadow about areference point near the center or the weighted center of the shadow,finds short curve lengths in a case where the closed curve is superposedon the shadow or short curve lengths in a case where the closed curve isnot superposed on the shadow as well as the number of the short curvelengths, and makes a decision as to whether the shadow is a focuscandidate shadow on the basis of the relationship between the shortcurve lengths and the number of the short curve lengths.
 20. An imagediagnosis supporting device according to claim 1, characterized in thatthe decision making means samples pixel values in the medical imagewhich intersect at least one straight line passing through the shadowand extending in a predetermined direction, find a positive or negativedensity gradient of each pixel on the straight line on the basis of thepixel values, defines the number of pixels in which the positivegradient continues as a positive run length and defines the number ofpixels by which the negative gradient continues as a negative runlength, finds positive and negative run lengths and the number of thepositive run lengths and the number of positive run lengths, and makes adecision as to whether the shadow is a focus candidate shadow on thebasis of the relationship between the positive and negative run lengthsand the number of the positive run lengths and the number of positiverun lengths.
 21. An image diagnosis supporting device according to claim1, characterized in that the decision making means finds a variance or astandard deviation of pixel values of a shadow in the multi-valuedimage, and makes a decision as to whether the shadow is a focuscandidate shadow on the basis of the variance or the standard deviation.22. An image diagnosis supporting device according to claim 21,characterized by detecting a center or a weighted center of a shadow onthe basis of the multi-valued image, rotating a straight line ofpredetermined length about a reference point near the center or theweighted center of the shadow to sample pixel values of the shadow whichintersect the straight line in the multi-valued image, and finding thestandard deviation of the pixel values which intersect the straight lineat a predetermined angle, for making the decision as to the shadow. 23.An image diagnosis supporting device according to claim 21,characterized by dividing a shadow in the multi-valued image into aplurality of regions and finding a variance or a standard deviation ofthe pixel values in each of the regions, for making the decision as tothe shadow.
 24. An image diagnosis supporting device according to claim21, characterized by finding an outside-shadow variance or anoutside-shadow standard deviation of pixel values in a predeterminedregion outside a shadow in the multi-valued image, for making thedecision as to the shadow.
 25. An image diagnosis supporting deviceaccording to claim 21, characterized by detecting a center or a weightedcenter of a shadow in the multi-valued image and finding a variance or astandard deviation of distance from the center or the weighted center toan edge of the shadow along the entire periphery of the shadow, formaking the decision as to the shadow.
 26. An image diagnosis supportingdevice according to claim 21, characterized by finding a variance or astandard deviation of a distance from an edge to an edge of a shadow inthe horizontal and vertical directions, for making the decision as tothe shadow.
 27. An image diagnosis supporting device according to claim1, characterized in that the extracting means performs processing whichdetects a center or a weighted center of a shadow on the basis of themulti-valued image, rotates a straight line of predetermined lengthabout a reference point near the center or the weighted center of ashadow on the multi-valued image, the medical image or the decisiontarget medical image, finds a maximum value of length of the portion ofthe straight line which intersects the shadow, sets a strip-shapedextended line approximately parallel with a straight line of the maximumvalue, and adds a predetermined value to a pixel memory located on thestrip-shaped extension, repeatedly executing this processing a number oftimes equal to the number of the shadows.
 28. An image diagnosissupporting device according to claim 1, characterized in that thedecision making means compares shadows existing in two multi-valuedimages adjacent to each other in a slice-thickness direction of themedical image, and makes a decision as to whether each of the shadows isa focus candidate shadow, on the basis of whether the shadows overlapeach other in more than a predetermined proportion.
 29. An imagediagnosis supporting device according to claim 1, characterized in thatthe decision making means uses at least two sets from among a set ofaxial images, a set of sagittal images and a set of coronal images whichare perpendicular to one another, and extracts a focus candidate shadowfrom each of the at least two sets and makes a decision as to whetherthe shadow is a focus candidate shadow, on the basis of the position ofthe focus candidate shadow extracted in each of the images.
 30. An imagediagnosis supporting device according to claim 29, characterized by, inthe case where the shadow is a focus candidate shadow, storing in amemory the coordinate position of the focus candidate shadow andinformation on the focus candidate shadow.
 31. An image diagnosissupporting device according to claim 1, characterized in that thedecision making means finds the area of the shadow region and also findsan area of a concave region formed in an edge portion of the shadowregion, finds a ratio of the area of the shadow region to the area ofthe concave region, and makes a decision as to whether the shadow is afocus candidate shadow, on the basis of the found ratio.
 32. An imagediagnosis supporting device according to claim 1, characterized in thatthe decision making means finds an area of a circle or a polygoninscribed in an edge of the shadow region and the area of separatedregions into which the shadow is divided by the circle or the polygon,and finds the ratio of the area of the circle or the polygon to the areaof the separated regions of the shadow, and makes a decision as towhether the shadow is a focus candidate shadow, on the basis of thefound ratio.
 33. An image diagnosis supporting device according to claim1, characterized in that the decision making means finds an area of acircle or a polygon circumscribed to the outer edge of the shadowregion, finds a ratio of the area of the circle to the area of theshadow, and makes a decision as to whether the shadow is a focuscandidate shadow, on the basis of the found ratio.
 34. An imagediagnosis supporting device according to claim 1, characterized in thatthe display means displays a shadow determined as the focus candidateshadow through the decision making processing routines in the medicalimage or in an area other than the medical image each time oneprocessing of the decision making processing routines is completed. 35.An image diagnosis supporting device according to claim 34,characterized in that a first display area for displaying a focuscandidate shadow or image supplementary information, a second displayarea for displaying an undetected image in which a focus candidate isnot detected or image supplementary information, and a third displayarea for displaying an image impossible to identify and imagesupplementary information are provided on a picture of the displaymeans.
 36. An image diagnosis supporting device according to claim 1,characterized in that the display means displays a magnified image of avicinity of the focus candidate shadow in the medical image or in anarea other than the medical image.
 37. An image diagnosis supportingdevice according to claim 1, characterized in that the display meansdisplays the medical image by controlling the order of display thereofaccording to the position of the focus candidate shadow in the medicalimage.
 38. An image diagnosis supporting device according to claim 1,characterized in that the display means displays the medical imageshaving the focus candidate shadow in an order controlled by a pointingdevice, according to the movement the pointing device in the medicalimage with the focus candidate shadow.
 39. An image diagnosis supportingdevice according to claim 1, characterized in that the display meansdisplays a marker line which encloses the extracted focus candidateshadow.
 40. An image diagnosis supporting device according to claim 39,characterized in that the extracting means calculates the probabilitythat a shadow is a focus, and the display means changes displays themarker line in the display on the basis of the focus certainty.
 41. Animage diagnosis supporting device according to claim 39, characterizedin that in the case where markers overlap to enclose a plurality offocus candidate shadows, the display means displays the markers with anoverlapping portion thereof erased.
 42. An image diagnosis supportingdevice according to claim 39, characterized in that the display meansperforms contrast emphasis processing or gamma emphasis processing onthe area enclosed with the marker and clearly displays the focuscandidate shadow.
 43. An image diagnosis supporting device according toclaim 39, characterized in that in the case where a hidden mode in whichdisplay of the marker is disabled is selected, the display means stopsdisplaying the marker and displays, on a picture, information indicatingthat the hidden mode is presently active, and automatically displays themarker when a predetermined time elapses after the hidden mode has beenstarted.
 44. An image diagnosis supporting device according to claim 1,characterized in that the extracting means finds length of contactbetween the extracted focus candidate shadow and a wall portion anddetermines on the basis of the length of contact whether the shadow is ashadow of an object accompanying a focus, the display means displayingthe shadow of an object accompanying a focus enclosed with a marker. 45.An image diagnosis supporting device according to claim 1, characterizedin that the display means displays focus candidate shadows respectivelyextracted from medical images photographed at mutually different pointsof time, with the respective focus candidate shadows enclosed withmarkers which makes it possible to discriminate between the points oftime of photography.
 46. An image diagnosis supporting device accordingto claim 1, characterized in that the display means displays a markerhaving an elliptical shape whose long-axis direction coincides with along-axis direction of the extracted focus candidate shadow, with thefocus candidate shadow enclosed with the marker.
 47. An image diagnosissupporting device according to claim 46, characterized in that in thecase where markers overlap one another to enclose a plurality of focuscandidate shadows, respectively, the display means displays the markerswith the overlapping portion thereof erased.
 48. An image diagnosissupporting device according to claim 46, characterized in that thedisplay means performs contrast emphasis processing or gamma emphasisprocessing on an area enclosed with the markers and clearly displays thefocus candidate shadows.
 49. An image diagnosis supporting deviceaccording to claim 46, characterized in that in the case where a hiddenmode in which display of the markers is disabled is selected, thedisplay means stops displaying the markers and displays informationindicating that the hidden mode is presently active, and automaticallydisplays the markers when a predetermined time elapses after the hiddenmode has been started.
 50. An image diagnosis supporting deviceaccording to claim 1, characterized in that the display means displays amedical image in which the focus candidate shadow exists and a medicalimage in which the focus candidate shadow does not exist, by movingimage display with different display times allocated for the respectivemedical images.
 51. An image diagnosis supporting device according toclaim 1, characterized in that the display means does not display amedical image which has not yet undergone shadow detection by a doctorso that it can be identified.